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appendix.md
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\newpage
\appendix \appendix
# Expected and observed cross section limits # Expected and observed cross section limits
@ -7,83 +8,84 @@
| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] | | Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
|------------|-----------------|------------------|------------------|-----------------| |------------|-----------------|------------------|------------------|-----------------|
| 1.6 | 0.10406 | 0.14720 | 0.07371 | 0.08165 | | 1.6 | 0.10 | 0.15 | 0.074 | 0.082 |
| 1.8 | 0.07656 | 0.10800 | 0.05441 | 0.04114 | | 1.8 | 0.077 | 0.11 | 0.054 | 0.041 |
| 2.0 | 0.05422 | 0.07605 | 0.03879 | 0.04043 | | 2.0 | 0.054 | 0.076 | 0.039 | 0.040 |
| 2.5 | 0.02430 | 0.03408 | 0.01747 | 0.04052 | | 2.5 | 0.024 | 0.034 | 0.017 | 0.041 |
| 3.0 | 0.01262 | 0.01775 | 0.00904 | 0.02109 | | 3.0 | 0.013 | 0.018 | 0.009 | 0.021 |
| 3.5 | 0.00703 | 0.00992 | 0.00502 | 0.00399 | | 3.5 | 0.0070 | 0.0099 | 0.005 | 0.004 |
| 4.0 | 0.00424 | 0.00603 | 0.00300 | 0.00172 | | 4.0 | 0.0042 | 0.0060 | 0.003 | 0.0017 |
| 4.5 | 0.00355 | 0.00478 | 0.00273 | 0.00249 | | 4.0 | 0.0042 | 0.0060 | 0.003 | 0.0017 |
| 5.0 | 0.00269 | 0.00357 | 0.00211 | 0.00240 | | 4.5 | 0.0035 | 0.0048 | 0.0027 | 0.0025 |
| 6.0 | 0.00103 | 0.00160 | 0.00068 | 0.00062 | | 5.0 | 0.0027 | 0.0036 | 0.0021 | 0.0024 |
| 7.0 | 0.00063 | 0.00105 | 0.00039 | 0.00086 | | 6.0 | 0.0010 | 0.0016 | 0.00068 | 0.00062 |
| 7.0 | 0.00063 | 0.0010 | 0.00039 | 0.00086 |
: Cross Section limits using 2016 data and the deep boosted tagger for the decay to qW : Cross Section limits using 2016 data and the deep boosted tagger for the decay to qW
| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] | | Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
|------------|-----------------|------------------|------------------|-----------------| |------------|-----------------|------------------|------------------|-----------------|
| 1.6 | 0.17750 | 0.25179 | 0.12572 | 0.38242 | | 1.6 | 0.18 | 0.25 | 0.13 | 0.38 |
| 1.8 | 0.11125 | 0.15870 | 0.07826 | 0.11692 | | 1.8 | 0.11 | 0.16 | 0.078 | 0.12 |
| 2.0 | 0.08188 | 0.11549 | 0.05799 | 0.09528 | | 2.0 | 0.082 | 0.12 | 0.058 | 0.095 |
| 2.5 | 0.03328 | 0.04668 | 0.02373 | 0.03653 | | 2.5 | 0.033 | 0.047 | 0.024 | 0.037 |
| 3.0 | 0.01648 | 0.02338 | 0.01181 | 0.01108 | | 3.0 | 0.016 | 0.023 | 0.012 | 0.011 |
| 3.5 | 0.00840 | 0.01195 | 0.00593 | 0.00683 | | 3.5 | 0.0084 | 0.012 | 0.0059 | 0.0068 |
| 4.0 | 0.00459 | 0.00666 | 0.00322 | 0.00342 | | 4.0 | 0.0046 | 0.0067 | 0.0032 | 0.0034 |
| 4.5 | 0.00276 | 0.00412 | 0.00190 | 0.00366 | | 4.5 | 0.0028 | 0.0041 | 0.0019 | 0.0037 |
| 5.0 | 0.00177 | 0.00271 | 0.00118 | 0.00401 | | 5.0 | 0.0018 | 0.0027 | 0.0012 | 0.0040 |
| 6.0 | 0.00110 | 0.00175 | 0.00071 | 0.00155 | | 6.0 | 0.0011 | 0.0017 | 0.00071 | 0.0016 |
| 7.0 | 0.00065 | 0.00108 | 0.00041 | 0.00108 | | 7.0 | 0.00065 | 0.0011 | 0.00041 | 0.0011 |
: Cross Section limits using 2016 data and the N-subjettiness tagger for the decay to qZ : Cross Section limits using 2016 data and the N-subjettiness tagger for the decay to qZ
| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] | | Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
|------------|-----------------|------------------|------------------|-----------------| |------------|-----------------|------------------|------------------|-----------------|
| 1.6 | 0.08687 | 0.12254 | 0.06174 | 0.06987 | | 1.6 | 0.087 | 0.12 | 0.062 | 0.07 |
| 1.8 | 0.06719 | 0.09477 | 0.04832 | 0.03424 | | 1.8 | 0.067 | 0.095 | 0.048 | 0.034 |
| 2.0 | 0.04734 | 0.06640 | 0.03405 | 0.03310 | | 2.0 | 0.047 | 0.066 | 0.034 | 0.033 |
| 2.5 | 0.01867 | 0.02619 | 0.01343 | 0.03214 | | 2.5 | 0.019 | 0.026 | 0.013 | 0.032 |
| 3.0 | 0.01043 | 0.01463 | 0.00744 | 0.01773 | | 3.0 | 0.010 | 0.015 | 0.0074 | 0.018 |
| 3.5 | 0.00596 | 0.00840 | 0.00426 | 0.00347 | | 3.5 | 0.0060 | 0.0084 | 0.0043 | 0.0035 |
| 4.0 | 0.00353 | 0.00500 | 0.00250 | 0.00140 | | 4.0 | 0.0035 | 0.0050 | 0.0025 | 0.0014 |
| 4.5 | 0.00233 | 0.00335 | 0.00164 | 0.00181 | | 4.5 | 0.0023 | 0.0034 | 0.0016 | 0.0018 |
| 5.0 | 0.00157 | 0.00231 | 0.00110 | 0.00188 | | 5.0 | 0.0016 | 0.0023 | 0.0011 | 0.0019 |
| 6.0 | 0.00082 | 0.00126 | 0.00054 | 0.00049 | | 6.0 | 0.00082 | 0.0013 | 0.00054 | 0.00049 |
| 7.0 | 0.00050 | 0.00083 | 0.00031 | 0.00066 | | 7.0 | 0.00050 | 0.00083 | 0.00031 | 0.00066 |
: Cross Section limits using 2016 data and deep boosted tagger for the decay to qZ : Cross Section limits using 2016 data and deep boosted tagger for the decay to qZ
| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] | | Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
|------------|-----------------|------------------|------------------|-----------------| |------------|-----------------|------------------|------------------|-----------------|
| 1.6 | 0.16687 | 0.23805 | 0.11699 | 0.35999 | | 1.6 | 0.15 | 0.22 | 0.11 | 0.33 |
| 1.8 | 0.12750 | 0.17934 | 0.09138 | 0.12891 | | 1.8 | 0.10 | 0.14 | 0.072 | 0.085 |
| 2.0 | 0.09062 | 0.12783 | 0.06474 | 0.09977 | | 2.0 | 0.077 | 0.11 | 0.056 | 0.064 |
| 2.5 | 0.03391 | 0.04783 | 0.02422 | 0.03754 | | 2.5 | 0.027 | 0.038 | 0.019 | 0.041 |
| 3.0 | 0.01781 | 0.02513 | 0.01277 | 0.01159 | | 3.0 | 0.015 | 0.021 | 0.010 | 0.0087 |
| 3.5 | 0.00949 | 0.01346 | 0.00678 | 0.00741 | | 3.5 | 0.0084 | 0.012 | 0.006 | 0.0066 |
| 4.0 | 0.00494 | 0.00711 | 0.00349 | 0.00362 | | 4.0 | 0.0049 | 0.0071 | 0.0035 | 0.0045 |
| 4.5 | 0.00293 | 0.00429 | 0.00203 | 0.00368 | | 4.5 | 0.0032 | 0.0046 | 0.0022 | 0.0026 |
| 5.0 | 0.00188 | 0.00284 | 0.00127 | 0.00426 | | 5.0 | 0.0022 | 0.0033 | 0.0015 | 0.0041 |
| 6.0 | 0.00102 | 0.00161 | 0.00066 | 0.00155 | | 6.0 | 0.0012 | 0.0019 | 0.00081 | 0.0018 |
| 7.0 | 0.00053 | 0.00085 | 0.00034 | 0.00085 | | 7.0 | 0.00057 | 0.00092 | 0.00037 | 0.00093 |
: Cross Section limits using the combined data and the N-subjettiness tagger for the decay to qW : Cross Section limits using the combined data and the N-subjettiness tagger for the decay to qW
| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] | | Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
|------------|-----------------|------------------|------------------|-----------------| |------------|-----------------|------------------|------------------|-----------------|
| 1.6 | 0.05703 | 0.07999 | 0.04088 | 0.03366 | | 1.6 | 0.057 | 0.08 | 0.041 | 0.034 |
| 1.8 | 0.03953 | 0.05576 | 0.02833 | 0.04319 | | 1.8 | 0.040 | 0.056 | 0.028 | 0.043 |
| 2.0 | 0.02844 | 0.03989 | 0.02045 | 0.04755 | | 2.0 | 0.028 | 0.040 | 0.020 | 0.048 |
| 2.5 | 0.01270 | 0.01781 | 0.00913 | 0.01519 | | 2.5 | 0.013 | 0.018 | 0.0091 | 0.015 |
| 3.0 | 0.00658 | 0.00923 | 0.00473 | 0.01218 | | 3.0 | 0.0066 | 0.0092 | 0.0047 | 0.012 |
| 3.5 | 0.00376 | 0.00529 | 0.00269 | 0.00474 | | 3.5 | 0.0038 | 0.0053 | 0.0027 | 0.0047 |
| 4.0 | 0.00218 | 0.00309 | 0.00156 | 0.00114 | | 4.0 | 0.0022 | 0.0031 | 0.0016 | 0.0011 |
| 4.5 | 0.00132 | 0.00188 | 0.00094 | 0.00068 | | 4.5 | 0.0013 | 0.0019 | 0.00094 | 0.00068 |
| 5.0 | 0.00084 | 0.00122 | 0.00060 | 0.00059 | | 5.0 | 0.00084 | 0.0012 | 0.00060 | 0.00059 |
| 6.0 | 0.00044 | 0.00066 | 0.00030 | 0.00041 | | 6.0 | 0.00044 | 0.00066 | 0.00030 | 0.00041 |
| 7.0 | 0.00022 | 0.00036 | 0.00014 | 0.00043 | | 7.0 | 0.00022 | 0.00036 | 0.00014 | 0.00043 |
@ -92,49 +94,66 @@
| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] | | Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
|------------|-----------------|------------------|------------------|-----------------| |------------|-----------------|------------------|------------------|-----------------|
| 1.6 | 0.06656 | 0.09495 | 0.04698 | 0.12374 | | 1.6 | 0.067 | 0.095 | 0.047 | 0.12 |
| 1.8 | 0.04281 | 0.06141 | 0.03001 | 0.05422 | | 1.8 | 0.043 | 0.061 | 0.030 | 0.054 |
| 2.0 | 0.03297 | 0.04650 | 0.02363 | 0.04658 | | 2.0 | 0.033 | 0.047 | 0.024 | 0.047 |
| 2.5 | 0.01328 | 0.01868 | 0.00950 | 0.01109 | | 2.5 | 0.013 | 0.019 | 0.0095 | 0.011 |
| 3.0 | 0.00650 | 0.00917 | 0.00464 | 0.00502 | | 3.0 | 0.0065 | 0.0092 | 0.0046 | 0.0050 |
| 3.5 | 0.00338 | 0.00479 | 0.00241 | 0.00408 | | 3.5 | 0.0034 | 0.0048 | 0.0024 | 0.0041 |
| 4.0 | 0.00182 | 0.00261 | 0.00129 | 0.00127 | | 4.0 | 0.0018 | 0.0026 | 0.0013 | 0.0013 |
| 4.5 | 0.00107 | 0.00156 | 0.00074 | 0.00123 | | 4.5 | 0.0011 | 0.0016 | 0.00074 | 0.0012 |
| 5.0 | 0.00068 | 0.00102 | 0.00046 | 0.00149 | | 5.0 | 0.00068 | 0.0010 | 0.00046 | 0.0015 |
| 6.0 | 0.00038 | 0.00060 | 0.00024 | 0.00034 | | 6.0 | 0.00038 | 0.00060 | 0.00024 | 0.00034 |
| 7.0 | 0.00021 | 0.00035 | 0.00013 | 0.00046 | | 7.0 | 0.00021 | 0.00035 | 0.00013 | 0.00046 |
: Cross Section limits using the combined data and the N-subjettiness tagger for the decay to qZ : Cross Section limits using the combined data and the N-subjettiness tagger for the decay to qZ
| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] | | Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
|------------|-----------------|------------------|------------------|-----------------| |------------|-----------------|------------------|------------------|-----------------|
| 1.6 | 0.05125 | 0.07188 | 0.03667 | 0.02993 | | 1.6 | 0.051 | 0.072 | 0.037 | 0.030 |
| 1.8 | 0.03547 | 0.04989 | 0.02551 | 0.03614 | | 1.8 | 0.035 | 0.050 | 0.026 | 0.036 |
| 2.0 | 0.02523 | 0.03539 | 0.01815 | 0.04177 | | 2.0 | 0.025 | 0.035 | 0.018 | 0.042 |
| 2.5 | 0.01059 | 0.01485 | 0.00761 | 0.01230 | | 2.5 | 0.011 | 0.015 | 0.0076 | 0.012 |
| 3.0 | 0.00576 | 0.00808 | 0.00412 | 0.01087 | | 3.0 | 0.0058 | 0.0081 | 0.0041 | 0.011 |
| 3.5 | 0.00327 | 0.00460 | 0.00234 | 0.00425 | | 3.5 | 0.0033 | 0.0046 | 0.0023 | 0.0042 |
| 4.0 | 0.00190 | 0.00269 | 0.00136 | 0.00097 | | 4.0 | 0.0019 | 0.0027 | 0.0014 | 0.00097 |
| 4.5 | 0.00119 | 0.00168 | 0.00084 | 0.00059 | | 4.5 | 0.0012 | 0.0017 | 0.00084 | 0.00059 |
| 5.0 | 0.00077 | 0.00110 | 0.00054 | 0.00051 | | 5.0 | 0.00077 | 0.0011 | 0.00054 | 0.00051 |
| 6.0 | 0.00039 | 0.00057 | 0.00026 | 0.00036 | | 6.0 | 0.00039 | 0.00057 | 0.00026 | 0.00036 |
| 7.0 | 0.00019 | 0.00031 | 0.00013 | 0.00036 | | 7.0 | 0.00019 | 0.00031 | 0.00013 | 0.00036 |
: Cross Section limits using the combined data and deep boosted tagger for the decay to qZ : Cross Section limits using the combined data and deep boosted tagger for the decay to qZ
| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] | | Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
|------------|-----------------|------------------|------------------|-----------------| |------------|-----------------|------------------|------------------|-----------------|
| 1.6 | 0.07719 | 0.10949 | 0.05467 | 0.14090 | | 1.6 | 0.067 | 0.095 | 0.047 | 0.095 |
| 1.8 | 0.05297 | 0.07493 | 0.03752 | 0.06690 | | 1.8 | 0.044 | 0.063 | 0.032 | 0.048 |
| 2.0 | 0.03875 | 0.05466 | 0.02768 | 0.05855 | | 2.0 | 0.032 | 0.045 | 0.023 | 0.045 |
| 2.5 | 0.01512 | 0.02126 | 0.01080 | 0.01160 | | 2.5 | 0.012 | 0.017 | 0.0088 | 0.013 |
| 3.0 | 0.00773 | 0.01088 | 0.00554 | 0.00548 | | 3.0 | 0.0064 | 0.009 | 0.0046 | 0.0032 |
| 3.5 | 0.00400 | 0.00565 | 0.00285 | 0.00465 | | 3.5 | 0.0036 | 0.0051 | 0.0026 | 0.0039 |
| 4.0 | 0.00211 | 0.00301 | 0.00149 | 0.00152 | | 4.0 | 0.0021 | 0.0029 | 0.0015 | 0.0027 |
| 4.5 | 0.00118 | 0.00172 | 0.00082 | 0.00128 | | 4.5 | 0.0013 | 0.0018 | 0.00088 | 0.00094 |
| 5.0 | 0.00073 | 0.00108 | 0.00050 | 0.00161 | | 5.0 | 0.00083 | 0.0012 | 0.00057 | 0.00150 |
| 6.0 | 0.00039 | 0.00060 | 0.00025 | 0.00036 | | 6.0 | 0.00046 | 0.00072 | 0.00031 | 0.00043 |
| 7.0 | 0.00021 | 0.00034 | 0.00013 | 0.00045 | | 7.0 | 0.00023 | 0.00037 | 0.00015 | 0.00049 |
\newpage
\section*{Erklärung}
Hiermit bestätige ich, dass die vorliegende Bachelorarbeit von mir selbstständig verfasst wurde und ich keine anderen
als die angegebenen Hilfsmittel - insbesondere keine im Quellenverzeichnis nicht benannten Internet-Quellen - benutzt
habe. Die Arbeit wurde bisher weder gesamt noch in Teilen einer anderen Prüfungsbehörde vorgelegt. Die eingereichte
schriftliche Fassung entspricht der auf dem elekronischen Speichermedium. Ich bin damit einverstanden, dass die
Bachelorarbeit veröffentlicht wird.
\vspace{5cm}
\parbox{5cm}{\hrule
\strut \footnotesize Ort, Datum} \hspace{1cm}\parbox{5cm}{\hrule
\strut \footnotesize David Leppla-Weber}

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appendix.tex
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\newpage
\appendix \appendix
\hypertarget{expected-and-observed-cross-section-limits}{% \hypertarget{expected-and-observed-cross-section-limits}{%
@ -17,17 +18,18 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.10406 & 0.14720 & 0.07371 & 0.08165\tabularnewline 1.6 & 0.10 & 0.15 & 0.074 & 0.082\tabularnewline
1.8 & 0.07656 & 0.10800 & 0.05441 & 0.04114\tabularnewline 1.8 & 0.077 & 0.11 & 0.054 & 0.041\tabularnewline
2.0 & 0.05422 & 0.07605 & 0.03879 & 0.04043\tabularnewline 2.0 & 0.054 & 0.076 & 0.039 & 0.040\tabularnewline
2.5 & 0.02430 & 0.03408 & 0.01747 & 0.04052\tabularnewline 2.5 & 0.024 & 0.034 & 0.017 & 0.041\tabularnewline
3.0 & 0.01262 & 0.01775 & 0.00904 & 0.02109\tabularnewline 3.0 & 0.013 & 0.018 & 0.009 & 0.021\tabularnewline
3.5 & 0.00703 & 0.00992 & 0.00502 & 0.00399\tabularnewline 3.5 & 0.0070 & 0.0099 & 0.005 & 0.004\tabularnewline
4.0 & 0.00424 & 0.00603 & 0.00300 & 0.00172\tabularnewline 4.0 & 0.0042 & 0.0060 & 0.003 & 0.0017\tabularnewline
4.5 & 0.00355 & 0.00478 & 0.00273 & 0.00249\tabularnewline 4.0 & 0.0042 & 0.0060 & 0.003 & 0.0017\tabularnewline
5.0 & 0.00269 & 0.00357 & 0.00211 & 0.00240\tabularnewline 4.5 & 0.0035 & 0.0048 & 0.0027 & 0.0025\tabularnewline
6.0 & 0.00103 & 0.00160 & 0.00068 & 0.00062\tabularnewline 5.0 & 0.0027 & 0.0036 & 0.0021 & 0.0024\tabularnewline
7.0 & 0.00063 & 0.00105 & 0.00039 & 0.00086\tabularnewline 6.0 & 0.0010 & 0.0016 & 0.00068 & 0.00062\tabularnewline
7.0 & 0.00063 & 0.0010 & 0.00039 & 0.00086\tabularnewline
\bottomrule \bottomrule
\end{longtable} \end{longtable}
@ -44,17 +46,17 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.17750 & 0.25179 & 0.12572 & 0.38242\tabularnewline 1.6 & 0.18 & 0.25 & 0.13 & 0.38\tabularnewline
1.8 & 0.11125 & 0.15870 & 0.07826 & 0.11692\tabularnewline 1.8 & 0.11 & 0.16 & 0.078 & 0.12\tabularnewline
2.0 & 0.08188 & 0.11549 & 0.05799 & 0.09528\tabularnewline 2.0 & 0.082 & 0.12 & 0.058 & 0.095\tabularnewline
2.5 & 0.03328 & 0.04668 & 0.02373 & 0.03653\tabularnewline 2.5 & 0.033 & 0.047 & 0.024 & 0.037\tabularnewline
3.0 & 0.01648 & 0.02338 & 0.01181 & 0.01108\tabularnewline 3.0 & 0.016 & 0.023 & 0.012 & 0.011\tabularnewline
3.5 & 0.00840 & 0.01195 & 0.00593 & 0.00683\tabularnewline 3.5 & 0.0084 & 0.012 & 0.0059 & 0.0068\tabularnewline
4.0 & 0.00459 & 0.00666 & 0.00322 & 0.00342\tabularnewline 4.0 & 0.0046 & 0.0067 & 0.0032 & 0.0034\tabularnewline
4.5 & 0.00276 & 0.00412 & 0.00190 & 0.00366\tabularnewline 4.5 & 0.0028 & 0.0041 & 0.0019 & 0.0037\tabularnewline
5.0 & 0.00177 & 0.00271 & 0.00118 & 0.00401\tabularnewline 5.0 & 0.0018 & 0.0027 & 0.0012 & 0.0040\tabularnewline
6.0 & 0.00110 & 0.00175 & 0.00071 & 0.00155\tabularnewline 6.0 & 0.0011 & 0.0017 & 0.00071 & 0.0016\tabularnewline
7.0 & 0.00065 & 0.00108 & 0.00041 & 0.00108\tabularnewline 7.0 & 0.00065 & 0.0011 & 0.00041 & 0.0011\tabularnewline
\bottomrule \bottomrule
\end{longtable} \end{longtable}
@ -71,16 +73,16 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.08687 & 0.12254 & 0.06174 & 0.06987\tabularnewline 1.6 & 0.087 & 0.12 & 0.062 & 0.07\tabularnewline
1.8 & 0.06719 & 0.09477 & 0.04832 & 0.03424\tabularnewline 1.8 & 0.067 & 0.095 & 0.048 & 0.034\tabularnewline
2.0 & 0.04734 & 0.06640 & 0.03405 & 0.03310\tabularnewline 2.0 & 0.047 & 0.066 & 0.034 & 0.033\tabularnewline
2.5 & 0.01867 & 0.02619 & 0.01343 & 0.03214\tabularnewline 2.5 & 0.019 & 0.026 & 0.013 & 0.032\tabularnewline
3.0 & 0.01043 & 0.01463 & 0.00744 & 0.01773\tabularnewline 3.0 & 0.010 & 0.015 & 0.0074 & 0.018\tabularnewline
3.5 & 0.00596 & 0.00840 & 0.00426 & 0.00347\tabularnewline 3.5 & 0.0060 & 0.0084 & 0.0043 & 0.0035\tabularnewline
4.0 & 0.00353 & 0.00500 & 0.00250 & 0.00140\tabularnewline 4.0 & 0.0035 & 0.0050 & 0.0025 & 0.0014\tabularnewline
4.5 & 0.00233 & 0.00335 & 0.00164 & 0.00181\tabularnewline 4.5 & 0.0023 & 0.0034 & 0.0016 & 0.0018\tabularnewline
5.0 & 0.00157 & 0.00231 & 0.00110 & 0.00188\tabularnewline 5.0 & 0.0016 & 0.0023 & 0.0011 & 0.0019\tabularnewline
6.0 & 0.00082 & 0.00126 & 0.00054 & 0.00049\tabularnewline 6.0 & 0.00082 & 0.0013 & 0.00054 & 0.00049\tabularnewline
7.0 & 0.00050 & 0.00083 & 0.00031 & 0.00066\tabularnewline 7.0 & 0.00050 & 0.00083 & 0.00031 & 0.00066\tabularnewline
\bottomrule \bottomrule
\end{longtable} \end{longtable}
@ -98,17 +100,17 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.16687 & 0.23805 & 0.11699 & 0.35999\tabularnewline 1.6 & 0.15 & 0.22 & 0.11 & 0.33\tabularnewline
1.8 & 0.12750 & 0.17934 & 0.09138 & 0.12891\tabularnewline 1.8 & 0.10 & 0.14 & 0.072 & 0.085\tabularnewline
2.0 & 0.09062 & 0.12783 & 0.06474 & 0.09977\tabularnewline 2.0 & 0.077 & 0.11 & 0.056 & 0.064\tabularnewline
2.5 & 0.03391 & 0.04783 & 0.02422 & 0.03754\tabularnewline 2.5 & 0.027 & 0.038 & 0.019 & 0.041\tabularnewline
3.0 & 0.01781 & 0.02513 & 0.01277 & 0.01159\tabularnewline 3.0 & 0.015 & 0.021 & 0.010 & 0.0087\tabularnewline
3.5 & 0.00949 & 0.01346 & 0.00678 & 0.00741\tabularnewline 3.5 & 0.0084 & 0.012 & 0.006 & 0.0066\tabularnewline
4.0 & 0.00494 & 0.00711 & 0.00349 & 0.00362\tabularnewline 4.0 & 0.0049 & 0.0071 & 0.0035 & 0.0045\tabularnewline
4.5 & 0.00293 & 0.00429 & 0.00203 & 0.00368\tabularnewline 4.5 & 0.0032 & 0.0046 & 0.0022 & 0.0026\tabularnewline
5.0 & 0.00188 & 0.00284 & 0.00127 & 0.00426\tabularnewline 5.0 & 0.0022 & 0.0033 & 0.0015 & 0.0041\tabularnewline
6.0 & 0.00102 & 0.00161 & 0.00066 & 0.00155\tabularnewline 6.0 & 0.0012 & 0.0019 & 0.00081 & 0.0018\tabularnewline
7.0 & 0.00053 & 0.00085 & 0.00034 & 0.00085\tabularnewline 7.0 & 0.00057 & 0.00092 & 0.00037 & 0.00093\tabularnewline
\bottomrule \bottomrule
\end{longtable} \end{longtable}
@ -125,15 +127,15 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.05703 & 0.07999 & 0.04088 & 0.03366\tabularnewline 1.6 & 0.057 & 0.08 & 0.041 & 0.034\tabularnewline
1.8 & 0.03953 & 0.05576 & 0.02833 & 0.04319\tabularnewline 1.8 & 0.040 & 0.056 & 0.028 & 0.043\tabularnewline
2.0 & 0.02844 & 0.03989 & 0.02045 & 0.04755\tabularnewline 2.0 & 0.028 & 0.040 & 0.020 & 0.048\tabularnewline
2.5 & 0.01270 & 0.01781 & 0.00913 & 0.01519\tabularnewline 2.5 & 0.013 & 0.018 & 0.0091 & 0.015\tabularnewline
3.0 & 0.00658 & 0.00923 & 0.00473 & 0.01218\tabularnewline 3.0 & 0.0066 & 0.0092 & 0.0047 & 0.012\tabularnewline
3.5 & 0.00376 & 0.00529 & 0.00269 & 0.00474\tabularnewline 3.5 & 0.0038 & 0.0053 & 0.0027 & 0.0047\tabularnewline
4.0 & 0.00218 & 0.00309 & 0.00156 & 0.00114\tabularnewline 4.0 & 0.0022 & 0.0031 & 0.0016 & 0.0011\tabularnewline
4.5 & 0.00132 & 0.00188 & 0.00094 & 0.00068\tabularnewline 4.5 & 0.0013 & 0.0019 & 0.00094 & 0.00068\tabularnewline
5.0 & 0.00084 & 0.00122 & 0.00060 & 0.00059\tabularnewline 5.0 & 0.00084 & 0.0012 & 0.00060 & 0.00059\tabularnewline
6.0 & 0.00044 & 0.00066 & 0.00030 & 0.00041\tabularnewline 6.0 & 0.00044 & 0.00066 & 0.00030 & 0.00041\tabularnewline
7.0 & 0.00022 & 0.00036 & 0.00014 & 0.00043\tabularnewline 7.0 & 0.00022 & 0.00036 & 0.00014 & 0.00043\tabularnewline
\bottomrule \bottomrule
@ -152,15 +154,15 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.06656 & 0.09495 & 0.04698 & 0.12374\tabularnewline 1.6 & 0.067 & 0.095 & 0.047 & 0.12\tabularnewline
1.8 & 0.04281 & 0.06141 & 0.03001 & 0.05422\tabularnewline 1.8 & 0.043 & 0.061 & 0.030 & 0.054\tabularnewline
2.0 & 0.03297 & 0.04650 & 0.02363 & 0.04658\tabularnewline 2.0 & 0.033 & 0.047 & 0.024 & 0.047\tabularnewline
2.5 & 0.01328 & 0.01868 & 0.00950 & 0.01109\tabularnewline 2.5 & 0.013 & 0.019 & 0.0095 & 0.011\tabularnewline
3.0 & 0.00650 & 0.00917 & 0.00464 & 0.00502\tabularnewline 3.0 & 0.0065 & 0.0092 & 0.0046 & 0.0050\tabularnewline
3.5 & 0.00338 & 0.00479 & 0.00241 & 0.00408\tabularnewline 3.5 & 0.0034 & 0.0048 & 0.0024 & 0.0041\tabularnewline
4.0 & 0.00182 & 0.00261 & 0.00129 & 0.00127\tabularnewline 4.0 & 0.0018 & 0.0026 & 0.0013 & 0.0013\tabularnewline
4.5 & 0.00107 & 0.00156 & 0.00074 & 0.00123\tabularnewline 4.5 & 0.0011 & 0.0016 & 0.00074 & 0.0012\tabularnewline
5.0 & 0.00068 & 0.00102 & 0.00046 & 0.00149\tabularnewline 5.0 & 0.00068 & 0.0010 & 0.00046 & 0.0015\tabularnewline
6.0 & 0.00038 & 0.00060 & 0.00024 & 0.00034\tabularnewline 6.0 & 0.00038 & 0.00060 & 0.00024 & 0.00034\tabularnewline
7.0 & 0.00021 & 0.00035 & 0.00013 & 0.00046\tabularnewline 7.0 & 0.00021 & 0.00035 & 0.00013 & 0.00046\tabularnewline
\bottomrule \bottomrule
@ -179,15 +181,15 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.05125 & 0.07188 & 0.03667 & 0.02993\tabularnewline 1.6 & 0.051 & 0.072 & 0.037 & 0.030\tabularnewline
1.8 & 0.03547 & 0.04989 & 0.02551 & 0.03614\tabularnewline 1.8 & 0.035 & 0.050 & 0.026 & 0.036\tabularnewline
2.0 & 0.02523 & 0.03539 & 0.01815 & 0.04177\tabularnewline 2.0 & 0.025 & 0.035 & 0.018 & 0.042\tabularnewline
2.5 & 0.01059 & 0.01485 & 0.00761 & 0.01230\tabularnewline 2.5 & 0.011 & 0.015 & 0.0076 & 0.012\tabularnewline
3.0 & 0.00576 & 0.00808 & 0.00412 & 0.01087\tabularnewline 3.0 & 0.0058 & 0.0081 & 0.0041 & 0.011\tabularnewline
3.5 & 0.00327 & 0.00460 & 0.00234 & 0.00425\tabularnewline 3.5 & 0.0033 & 0.0046 & 0.0023 & 0.0042\tabularnewline
4.0 & 0.00190 & 0.00269 & 0.00136 & 0.00097\tabularnewline 4.0 & 0.0019 & 0.0027 & 0.0014 & 0.00097\tabularnewline
4.5 & 0.00119 & 0.00168 & 0.00084 & 0.00059\tabularnewline 4.5 & 0.0012 & 0.0017 & 0.00084 & 0.00059\tabularnewline
5.0 & 0.00077 & 0.00110 & 0.00054 & 0.00051\tabularnewline 5.0 & 0.00077 & 0.0011 & 0.00054 & 0.00051\tabularnewline
6.0 & 0.00039 & 0.00057 & 0.00026 & 0.00036\tabularnewline 6.0 & 0.00039 & 0.00057 & 0.00026 & 0.00036\tabularnewline
7.0 & 0.00019 & 0.00031 & 0.00013 & 0.00036\tabularnewline 7.0 & 0.00019 & 0.00031 & 0.00013 & 0.00036\tabularnewline
\bottomrule \bottomrule
@ -206,16 +208,34 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.07719 & 0.10949 & 0.05467 & 0.14090\tabularnewline 1.6 & 0.067 & 0.095 & 0.047 & 0.095\tabularnewline
1.8 & 0.05297 & 0.07493 & 0.03752 & 0.06690\tabularnewline 1.8 & 0.044 & 0.063 & 0.032 & 0.048\tabularnewline
2.0 & 0.03875 & 0.05466 & 0.02768 & 0.05855\tabularnewline 2.0 & 0.032 & 0.045 & 0.023 & 0.045\tabularnewline
2.5 & 0.01512 & 0.02126 & 0.01080 & 0.01160\tabularnewline 2.5 & 0.012 & 0.017 & 0.0088 & 0.013\tabularnewline
3.0 & 0.00773 & 0.01088 & 0.00554 & 0.00548\tabularnewline 3.0 & 0.0064 & 0.009 & 0.0046 & 0.0032\tabularnewline
3.5 & 0.00400 & 0.00565 & 0.00285 & 0.00465\tabularnewline 3.5 & 0.0036 & 0.0051 & 0.0026 & 0.0039\tabularnewline
4.0 & 0.00211 & 0.00301 & 0.00149 & 0.00152\tabularnewline 4.0 & 0.0021 & 0.0029 & 0.0015 & 0.0027\tabularnewline
4.5 & 0.00118 & 0.00172 & 0.00082 & 0.00128\tabularnewline 4.5 & 0.0013 & 0.0018 & 0.00088 & 0.00094\tabularnewline
5.0 & 0.00073 & 0.00108 & 0.00050 & 0.00161\tabularnewline 5.0 & 0.00083 & 0.0012 & 0.00057 & 0.00150\tabularnewline
6.0 & 0.00039 & 0.00060 & 0.00025 & 0.00036\tabularnewline 6.0 & 0.00046 & 0.00072 & 0.00031 & 0.00043\tabularnewline
7.0 & 0.00021 & 0.00034 & 0.00013 & 0.00045\tabularnewline 7.0 & 0.00023 & 0.00037 & 0.00015 & 0.00049\tabularnewline
\bottomrule \bottomrule
\end{longtable} \end{longtable}
\newpage
\section*{Erklärung}
Hiermit bestätige ich, dass die vorliegende Bachelorarbeit von mir
selbstständig verfasst wurde und ich keine anderen als die angegebenen
Hilfsmittel - insbesondere keine im Quellenverzeichnis nicht benannten
Internet-Quellen - benutzt habe. Die Arbeit wurde bisher weder gesamt
noch in Teilen einer anderen Prüfungsbehörde vorgelegt. Die eingereichte
schriftliche Fassung entspricht der auf dem elekronischen
Speichermedium. Ich bin damit einverstanden, dass die Bachelorarbeit
veröffentlicht wird.
\vspace{5cm}
\parbox{5cm}{\hrule
\strut \footnotesize Ort, Datum} \hspace{1cm}\parbox{5cm}{\hrule
\strut \footnotesize David Leppla-Weber}

50
bibliography.bib
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@ -1649,3 +1649,53 @@ archivePrefix = {arXiv},
reportNumber = "CMS-NOTE-2005-001, CERN-CMS-NOTE-2005-001", reportNumber = "CMS-NOTE-2005-001, CERN-CMS-NOTE-2005-001",
SLACcitation = "%%CITATION = CMS-NOTE-2005-001;%%" SLACcitation = "%%CITATION = CMS-NOTE-2005-001;%%"
} }
@online{CMS_LUMI,
title={CMS Luminosity - Public Results},
intitution={CERN},
collaboration={CMS Collaboration},
year={2019},
url={https://twiki.cern.ch/twiki/bin/view/CMSPublic/LumiPublicResults},
note={Online; accessed Oct. 2019}
}
@misc{MADGRAPH,
title={The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations},
author={J. Alwall and R. Frederix and S. Frixione and V. Hirschi and F. Maltoni and O. Mattelaer and H. -S. Shao and T. Stelzer and P. Torrielli and M. Zaro},
year={2014},
eprint={1405.0301},
archivePrefix={arXiv},
primaryClass={hep-ph}
}
@misc{PYTHIA8,
title={An Introduction to PYTHIA 8.2},
author={Torbjörn Sjöstrand and Stefan Ask and Jesper R. Christiansen and Richard Corke and Nishita Desai and Philip Ilten and Stephen Mrenna and Stefan Prestel and Christine O. Rasmussen and Peter Z. Skands},
year={2014},
eprint={1410.3012},
archivePrefix={arXiv},
primaryClass={hep-ph}
}
@online{CLT,
title={Central limit theorem},
author={Encyclopedia of Mathematics},
year={2012},
url={http://www.encyclopediaofmath.org/index.php?title=Central_limit_theorem&oldid=26376},
note={Online; accessed Oct. 2019}
}
@techreport{LUMI_UNC,
title = "{CMS Luminosity Measurements for the 2016 Data Taking
Period}",
institution = "CERN",
collaboration = "CMS Collaboration",
address = "Geneva",
number = "CMS-PAS-LUM-17-001",
year = "2017",
reportNumber = "CMS-PAS-LUM-17-001",
url = "https://cds.cern.ch/record/2257069",
}
@online{JEC,
title={Recommended Jet Energy Corrections and Uncertainties For Data and MC},
year={2019},
intitution={CERN},
collaboration={CMS Collaboration},
url={https://twiki.cern.ch/twiki/bin/viewauth/CMS/JECDataMC},
note={Online; accessed Oct. 2019}
}

2
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@ -1,5 +1,5 @@
#!/bin/sh #!/bin/sh
rm -f thesis.aux thesis.bcf thesis.blg thesis.log thesis.run.xml thesis.tex thesis.toc rm -f thesis.aux thesis.bcf thesis.blg thesis.log thesis.run.xml thesis.tex thesis.toc thesis.bbl
pandoc thesis.md -o thesis.tex --biblatex --bibliography=bibliography.bib -N --listings --pdf-engine=lualatex -s --filter pandoc-crossref --include-after-body=appendix.tex pandoc thesis.md -o thesis.tex --biblatex --bibliography=bibliography.bib -N --listings --pdf-engine=lualatex -s --filter pandoc-crossref --include-after-body=appendix.tex
lualatex thesis lualatex thesis
biber thesis biber thesis

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@ -37,15 +37,15 @@
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\@writefile{lot}{\contentsline {table}{\numberline {10}{\ignorespaces Cross Section limits using the combined data and the N-subjettiness tagger for the decay to qZ\relax }}{37}{table.10}\protected@file@percent }
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227
thesis.bbl
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@ -56,6 +56,78 @@
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45
thesis.bcf
View File

@ -2004,30 +2004,37 @@
<bcf:citekey order="7">PREV_RESEARCH</bcf:citekey> <bcf:citekey order="7">PREV_RESEARCH</bcf:citekey>
<bcf:citekey order="8">LHC_MACHINE</bcf:citekey> <bcf:citekey order="8">LHC_MACHINE</bcf:citekey>
<bcf:citekey order="9">CMS_REPORT</bcf:citekey> <bcf:citekey order="9">CMS_REPORT</bcf:citekey>
<bcf:citekey order="10">CMS_PLOT</bcf:citekey> <bcf:citekey order="10">CMS_LUMI</bcf:citekey>
<bcf:citekey order="11">CMS_PLOT</bcf:citekey> <bcf:citekey order="11">CMS_PLOT</bcf:citekey>
<bcf:citekey order="12">COORD_PLOT</bcf:citekey> <bcf:citekey order="12">CMS_PLOT</bcf:citekey>
<bcf:citekey order="13">COORD_PLOT</bcf:citekey> <bcf:citekey order="13">COORD_PLOT</bcf:citekey>
<bcf:citekey order="14">PARTICLE_FLOW</bcf:citekey> <bcf:citekey order="14">COORD_PLOT</bcf:citekey>
<bcf:citekey order="15">ERECO</bcf:citekey> <bcf:citekey order="15">PARTICLE_FLOW</bcf:citekey>
<bcf:citekey order="16">ANTIKT</bcf:citekey> <bcf:citekey order="16">ERECO</bcf:citekey>
<bcf:citekey order="17">ANTIKT</bcf:citekey> <bcf:citekey order="17">ANTIKT</bcf:citekey>
<bcf:citekey order="18">ANTIKT</bcf:citekey> <bcf:citekey order="18">ANTIKT</bcf:citekey>
<bcf:citekey order="19">SDM</bcf:citekey> <bcf:citekey order="19">ANTIKT</bcf:citekey>
<bcf:citekey order="20">PREV_RESEARCH</bcf:citekey> <bcf:citekey order="20">SDM</bcf:citekey>
<bcf:citekey order="21">PREV_RESEARCH</bcf:citekey> <bcf:citekey order="21">PREV_RESEARCH</bcf:citekey>
<bcf:citekey order="22">MONTECARLO</bcf:citekey> <bcf:citekey order="22">PREV_RESEARCH</bcf:citekey>
<bcf:citekey order="23">QSTAR_THEORY</bcf:citekey> <bcf:citekey order="23">MONTECARLO</bcf:citekey>
<bcf:citekey order="24">TAU21</bcf:citekey> <bcf:citekey order="24">MADGRAPH</bcf:citekey>
<bcf:citekey order="25">DEEP_BOOSTED</bcf:citekey> <bcf:citekey order="25">PYTHIA8</bcf:citekey>
<bcf:citekey order="26">DEEP_BOOSTED</bcf:citekey> <bcf:citekey order="26">QSTAR_THEORY</bcf:citekey>
<bcf:citekey order="27">DEEP_BOOSTED</bcf:citekey> <bcf:citekey order="27">MADGRAPH</bcf:citekey>
<bcf:citekey order="28">ASYMPTOTIC_LIMIT</bcf:citekey> <bcf:citekey order="28">JEC</bcf:citekey>
<bcf:citekey order="29">QSTAR_THEORY</bcf:citekey> <bcf:citekey order="29">TAU21</bcf:citekey>
<bcf:citekey order="30">PREV_RESEARCH</bcf:citekey> <bcf:citekey order="30">DEEP_BOOSTED</bcf:citekey>
<bcf:citekey order="31">PREV_RESEARCH</bcf:citekey> <bcf:citekey order="31">DEEP_BOOSTED</bcf:citekey>
<bcf:citekey order="32">PREV_RESEARCH</bcf:citekey> <bcf:citekey order="32">DEEP_BOOSTED</bcf:citekey>
<bcf:citekey order="33">PREV_RESEARCH</bcf:citekey> <bcf:citekey order="33">CLT</bcf:citekey>
<bcf:citekey order="34">ASYMPTOTIC_LIMIT</bcf:citekey>
<bcf:citekey order="35">QSTAR_THEORY</bcf:citekey>
<bcf:citekey order="36">LUMI_UNC</bcf:citekey>
<bcf:citekey order="37">PREV_RESEARCH</bcf:citekey>
<bcf:citekey order="38">PREV_RESEARCH</bcf:citekey>
<bcf:citekey order="39">PREV_RESEARCH</bcf:citekey>
<bcf:citekey order="40">PREV_RESEARCH</bcf:citekey>
<bcf:citekey order="0" nocite="1">*</bcf:citekey> <bcf:citekey order="0" nocite="1">*</bcf:citekey>
</bcf:section> </bcf:section>
<!-- SORTING TEMPLATES --> <!-- SORTING TEMPLATES -->

54
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@ -1,29 +1,29 @@
[0] Config.pm:304> INFO - This is Biber 2.12 [0] Config.pm:304> INFO - This is Biber 2.12
[0] Config.pm:307> INFO - Logfile is 'thesis.blg' [0] Config.pm:307> INFO - Logfile is 'thesis.blg'
[20] biber:315> INFO - === Do Okt 31, 2019, 19:46:44 [21] biber:315> INFO - === Fr Nov 1, 2019, 11:53:58
[39] Biber.pm:371> INFO - Reading 'thesis.bcf' [38] Biber.pm:371> INFO - Reading 'thesis.bcf'
[97] Biber.pm:886> INFO - Using all citekeys in bib section 0 [93] Biber.pm:886> INFO - Using all citekeys in bib section 0
[107] Biber.pm:4093> INFO - Processing section 0 [104] Biber.pm:4093> INFO - Processing section 0
[117] Biber.pm:4254> INFO - Looking for bibtex format file 'bibliography.bib' for section 0 [113] Biber.pm:4254> INFO - Looking for bibtex format file 'bibliography.bib' for section 0
[126] bibtex.pm:1523> INFO - LaTeX decoding ... [122] bibtex.pm:1523> INFO - LaTeX decoding ...
[243] bibtex.pm:1340> INFO - Found BibTeX data source 'bibliography.bib' [242] bibtex.pm:1340> INFO - Found BibTeX data source 'bibliography.bib'
[254] Utils.pm:193> WARN - month field 'Aug' in entry 'PREV_RESEARCH' is not an integer - this will probably not sort properly. [2558] Utils.pm:193> WARN - month field 'Oct' in entry 'CMS_TRIGGER' is not an integer - this will probably not sort properly.
[263] Utils.pm:193> WARN - month field 'Nov' in entry 'HADRONIZATION' is not an integer - this will probably not sort properly. [2561] Utils.pm:193> WARN - month field 'Jan' in entry 'PARTICLE_FLOW' is not an integer - this will probably not sort properly.
[270] Utils.pm:193> WARN - month field 'Jan' in entry 'PARTICLE_FLOW' is not an integer - this will probably not sort properly. [2834] Utils.pm:193> WARN - month field 'Aug' in entry 'PARTICLE_PHYSICS' is not an integer - this will probably not sort properly.
[277] Utils.pm:193> WARN - month field 'Mar' in entry 'TAU21' is not an integer - this will probably not sort properly. [2837] Utils.pm:193> WARN - month field 'Aug' in entry 'PREV_RESEARCH' is not an integer - this will probably not sort properly.
[285] Utils.pm:193> WARN - month field 'May' in entry 'SDM' is not an integer - this will probably not sort properly. [5407] Utils.pm:193> WARN - month field 'Jun' in entry 'CMS_PLOT' is not an integer - this will probably not sort properly.
[292] Utils.pm:193> WARN - month field 'Feb' in entry 'ASYMPTOTIC_LIMIT' is not an integer - this will probably not sort properly. [5413] Utils.pm:193> WARN - month field 'Apr' in entry 'ANTIKT' is not an integer - this will probably not sort properly.
[298] Utils.pm:193> WARN - month field 'Apr' in entry 'ANTIKT' is not an integer - this will probably not sort properly. [5425] Utils.pm:193> WARN - month field 'May' in entry 'SDM' is not an integer - this will probably not sort properly.
[572] Utils.pm:193> WARN - month field 'Aug' in entry 'PARTICLE_PHYSICS' is not an integer - this will probably not sort properly. [5428] Utils.pm:193> WARN - month field 'Apr' in entry 'MONTECARLO' is not an integer - this will probably not sort properly.
[576] Utils.pm:193> WARN - month field 'Apr' in entry 'MONTECARLO' is not an integer - this will probably not sort properly. [5432] Utils.pm:193> WARN - month field 'Oct' in entry 'SUC_COMBINATION' is not an integer - this will probably not sort properly.
[579] Utils.pm:193> WARN - month field 'Oct' in entry 'CMS_TRIGGER' is not an integer - this will probably not sort properly. [5436] Utils.pm:193> WARN - month field 'Nov' in entry 'HADRONIZATION' is not an integer - this will probably not sort properly.
[3177] Utils.pm:193> WARN - month field 'Jun' in entry 'CMS_PLOT' is not an integer - this will probably not sort properly. [5444] Utils.pm:193> WARN - month field 'Feb' in entry 'ASYMPTOTIC_LIMIT' is not an integer - this will probably not sort properly.
[3181] Utils.pm:193> WARN - month field 'Oct' in entry 'SUC_COMBINATION' is not an integer - this will probably not sort properly. [5466] Utils.pm:193> WARN - month field 'Mar' in entry 'TAU21' is not an integer - this will probably not sort properly.
[5523] Utils.pm:193> WARN - month field 'aug' in entry 'LHC_MACHINE' is not an integer - this will probably not sort properly. [5481] Utils.pm:193> WARN - month field 'aug' in entry 'LHC_MACHINE' is not an integer - this will probably not sort properly.
[5662] UCollate.pm:68> INFO - Overriding locale 'en-GB' defaults 'variable = shifted' with 'variable = non-ignorable' [5627] UCollate.pm:68> INFO - Overriding locale 'en-GB' defaults 'variable = shifted' with 'variable = non-ignorable'
[5662] UCollate.pm:68> INFO - Overriding locale 'en-GB' defaults 'normalization = NFD' with 'normalization = prenormalized' [5627] UCollate.pm:68> INFO - Overriding locale 'en-GB' defaults 'normalization = NFD' with 'normalization = prenormalized'
[5662] Biber.pm:3921> INFO - Sorting list 'nty/global//global/global' of type 'entry' with template 'nty' and locale 'en-GB' [5627] Biber.pm:3921> INFO - Sorting list 'nty/global//global/global' of type 'entry' with template 'nty' and locale 'en-GB'
[5662] Biber.pm:3927> INFO - No sort tailoring available for locale 'en-GB' [5627] Biber.pm:3927> INFO - No sort tailoring available for locale 'en-GB'
[5817] bbl.pm:636> INFO - Writing 'thesis.bbl' with encoding 'UTF-8' [5781] bbl.pm:636> INFO - Writing 'thesis.bbl' with encoding 'UTF-8'
[13628] bbl.pm:739> INFO - Output to thesis.bbl [13507] bbl.pm:739> INFO - Output to thesis.bbl
[13630] Biber.pm:110> INFO - WARNINGS: 13 [13509] Biber.pm:110> INFO - WARNINGS: 13

309
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@ -77,7 +77,7 @@ to a mass of 6.1\ TeV (qW) resp. 5.5\ TeV (qZ) with a confidence level of 95 \%.
the limits found by a previous research of data with an integrated luminosity of $\SI{35.92}{\per\femto\barn}$ collected the limits found by a previous research of data with an integrated luminosity of $\SI{35.92}{\per\femto\barn}$ collected
by the CMS experiment in 2016, excluding the q* particle up to a mass of 5.0\ TeV resp. 4.7\ TeV. The DeepAK8 tagger is by the CMS experiment in 2016, excluding the q* particle up to a mass of 5.0\ TeV resp. 4.7\ TeV. The DeepAK8 tagger is
found to currently be at the same level as the N-subjettiness tagger, giving a $\SI{0.1}{\tera\eV}$ better result for found to currently be at the same level as the N-subjettiness tagger, giving a $\SI{0.1}{\tera\eV}$ better result for
the decay to qW but a by $\SI{0.6}{\tera\eV}$ worse one for the decay to qZ. By optimizing the neural network's training the decay to qW but a by $\SI{0.5}{\tera\eV}$ worse one for the decay to qZ. By optimising the neural network's training
for the datasets of 2016, 2017 and 2018, the sensitivity can likely be improved. for the datasets of 2016, 2017 and 2018, the sensitivity can likely be improved.
\end{abstract} \end{abstract}
@ -97,7 +97,7 @@ Tagger verglichen. Im Ergebnis kann keine signifikante Abweichung vom Standardmo
wird mit einem Konfidenzniveau von 95 \% bis zu einer Masse von 6.1\ TeV (qW) bzw. 5.5\ TeV (qZ) ausgeschlossen. Das Limit wird mit einem Konfidenzniveau von 95 \% bis zu einer Masse von 6.1\ TeV (qW) bzw. 5.5\ TeV (qZ) ausgeschlossen. Das Limit
liegt etwa 1\ TeV höher, als das anhand des $\SI{35.92}{\per\femto\barn}$ großen Datensatzes von 2016 gefundene von 5.0 liegt etwa 1\ TeV höher, als das anhand des $\SI{35.92}{\per\femto\barn}$ großen Datensatzes von 2016 gefundene von 5.0
TeV bzw. 4.7\ TeV. Beim Zerfall zu qW erzielt der DeepAK8 Tagger ein um $\SI{0.1}{\tera\eV}$ besseres Ergebnis, als der TeV bzw. 4.7\ TeV. Beim Zerfall zu qW erzielt der DeepAK8 Tagger ein um $\SI{0.1}{\tera\eV}$ besseres Ergebnis, als der
N-Subjettiness Tagger, beim Zerfall zu qZ jedoch ein um $\SI{0.6}{\tera\eV}$ schlechteres. Durch Verbesserung des N-Subjettiness Tagger, beim Zerfall zu qZ jedoch ein um $\SI{0.5}{\tera\eV}$ schlechteres. Durch Verbesserung des
Trainings des neuronalen Netzwerkes für die drei Datensätze von 2016, 2017 und 2018, gibt es aber noch Potential die Trainings des neuronalen Netzwerkes für die drei Datensätze von 2016, 2017 und 2018, gibt es aber noch Potential die
Sensitivität zu verbessern. Sensitivität zu verbessern.
@ -162,13 +162,13 @@ integer but so far is only known to be one for fermions and either one (gauge bo
bosons. Fermions are further classified into quarks and leptons. bosons. Fermions are further classified into quarks and leptons.
Quarks and leptons can also be categorized into three generations, each of which contains two particles, also called Quarks and leptons can also be categorized into three generations, each of which contains two particles, also called
flavours. For leptons, the three generations each consist of a charged lepton and its corresponding neutrino, namely the flavours. For leptons, the three generations each consist of a charged lepton and its corresponding neutrino, namely the
electron, the muon and the tau. The three quark generations consist of first, the up and down, second, the charm and electron, the muon and the tau. The three quark generations consist of the up and down, the charm and strange, and the
strange, and third, the top and bottom quark. A full list of particles of the standard model can be found in [@fig:sm]. top and bottom quark. A full list of particles of the standard model can be found in [@fig:sm]. Furthermore, all
Furthermore, all fermions have an associated anti particle with reversed charge. Bound states of multiple quarks also fermions have an associated anti particle with reversed charge. Bound states of multiple quarks also exist and are
exist and are called hadrons. called hadrons.
![ ![
Elementary particles of the Standard Model and their mass charge and spin. Taken from [@SM] Elementary particles of the Standard Model and their mass charge and spin [@SM].
](./figures/sm_wikipedia.pdf){width=50% #fig:sm} ](./figures/sm_wikipedia.pdf){width=50% #fig:sm}
The gauge bosons, namely the photon, $W^\pm$ bosons, $Z^0$ boson, and gluon, are mediators of the different The gauge bosons, namely the photon, $W^\pm$ bosons, $Z^0$ boson, and gluon, are mediators of the different
@ -355,10 +355,9 @@ For that, it has an onion like setup, as can be seen in [@fig:cms_setup]. The pa
through a tracking system. They then pass an electromagnetic as well as a hadronic calorimeter. This part is surrounded through a tracking system. They then pass an electromagnetic as well as a hadronic calorimeter. This part is surrounded
by a superconducting solenoid that generates a magenetic field of 3.8 T. Outside of the solenoid are big muon chambers. by a superconducting solenoid that generates a magenetic field of 3.8 T. Outside of the solenoid are big muon chambers.
In 2016 the CMS captured data of an integrated luminosity of $\SI{37.80}{\per\femto\barn}$. In 2017 it collected In 2016 the CMS captured data of an integrated luminosity of $\SI{37.80}{\per\femto\barn}$. In 2017 it collected
$\SI{44.98}{\per\femto\barn}$ and in 2018 $\SI{63.67}{\per\femto\barn}$. Because of eventual inconsistencies in the $\SI{44.98}{\per\femto\barn}$ and in 2018 $\SI{63.67}{\per\femto\barn}$ [@CMS_LUMI]. The amount of data usable for
setup, some data have to be discarded. The amount of usable data is $\SI{34.92}{\per\femto\barn}$, research is $\SI{35.92}{\per\femto\barn}$, $\SI{41.53}{\per\femto\barn}$ and $\SI{59.74}{\per\femto\barn}$ for the years
$\SI{41.53}{\per\femto\barn}$ and $\SI{59.74}{\per\femto\barn}$ for the years 2016, 2017 and 2018, totalling to 2016, 2017 and 2018, totalling to $\SI{137.19}{\per\femto\barn}$ of data.
$\SI{137.19}{\per\femto\barn}$ of data.
![ ![
The setup of the Compact Muon Solenoid showing its onion like structure, the different detector parts and where The setup of the Compact Muon Solenoid showing its onion like structure, the different detector parts and where
@ -378,7 +377,7 @@ therefore, due to conservation of energy, the sum of all transverse momenta afte
this is not the case for the detected events, it implies particles that weren't detected such as neutrinos. this is not the case for the detected events, it implies particles that weren't detected such as neutrinos.
![Coordinate conventions of the CMS illustrating the use of $\eta$ and ![Coordinate conventions of the CMS illustrating the use of $\eta$ and
$\phi$. The Z axis is in beam direction. Taken from [@COORD_PLOT] $\phi$. The Z axis is in beam direction [@COORD_PLOT].
](./figures/cms_coordinates.png){#fig:cmscoords width=60%} ](./figures/cms_coordinates.png){#fig:cmscoords width=60%}
### The tracking system ### The tracking system
@ -450,7 +449,7 @@ chosen for this thesis. For this analysis, a radius of 0.8 is used.
![ ![
Comparison of the $k_t$, Cambridge/Aachen, SISCone and anti-$k_t$ algorithms clustering a sample parton-level event Comparison of the $k_t$, Cambridge/Aachen, SISCone and anti-$k_t$ algorithms clustering a sample parton-level event
with many random soft "ghosts". Taken from [@ANTIKT] with many random soft "ghosts" [@ANTIKT].
](./figures/antikt-comparision.png){#fig:antiktcomparison} ](./figures/antikt-comparision.png){#fig:antiktcomparison}
Furthermore, to approximate the mass of a heavy particle that caused a jet, the soft-drop mass [@SDM] can be used. In Furthermore, to approximate the mass of a heavy particle that caused a jet, the soft-drop mass [@SDM] can be used. In
@ -493,13 +492,13 @@ Before looking at the data collected by the CMS experiment, Monte Carlo simulati
signal are used to understand how the data is expected to look like. To replicate the QCD background processes, the signal are used to understand how the data is expected to look like. To replicate the QCD background processes, the
different particle interactions that take place in a proton - proton collision are simulated using the probabilities different particle interactions that take place in a proton - proton collision are simulated using the probabilities
provided by the Standard Model by calculating the cross sections of the different Feynman diagrams. This was done using provided by the Standard Model by calculating the cross sections of the different Feynman diagrams. This was done using
MadGraph and Pythia 8. Later on, also detector effects (like its limited resolution) are applied to make sure, they look MadGraph [@MADGRAPH] and Pythia 8 [@PYTHIA8]. Later on, also detector effects (like its limited resolution) are applied
like real data coming from the CMS detector. to make sure, they look like real data coming from the CMS detector.
The q\* signal samples are simulated by the probabilities given by the q\* theory [@QSTAR_THEORY] and assuming a cross The q\* signal samples are simulated by the probabilities given by the q\* theory [@QSTAR_THEORY] and assuming a cross
section of $\SI{1}{\per\pico\barn}$. The simulation was done using MadGraph for eleven masspoints between 1.6 TeV and 7 section of $\SI{1}{\pico\barn}$. The simulation was done using MadGraph [@MADGRAPH] for eleven masspoints between 1.6
TeV. Because of the expected high mass, the signal width will be dominated by the resolution of the detector, not by the TeV and 7 TeV. Because of the expected high mass, the signal width will be dominated by the resolution of the detector,
natural resonance width. not by the natural resonance width.
The dijet invariant mass distribution of the QCD background is expected to smoothly fall with higher masses. The dijet invariant mass distribution of the QCD background is expected to smoothly fall with higher masses.
It is therefore fitted using the following smooth falling function with three parameters p0, p1, p2: It is therefore fitted using the following smooth falling function with three parameters p0, p1, p2:
@ -519,14 +518,14 @@ The signal is fitted using a double sided crystal ball function. It has six para
A gaussian and a poisson function have also been studied but found to be not able to reproduce the signal shape as they A gaussian and a poisson function have also been studied but found to be not able to reproduce the signal shape as they
couldn't model the tails on both sides of the peak. couldn't model the tails on both sides of the peak.
A linear combination of the signal and background function is then fitted to a toy dataset with gaussian errors and a A linear combination of the signal and background function is then fitted to a toy dataset with gaussian errors obtained
simulated signal cross section of $\SI{1}{\per\pico\barn}$. The resulting coefficients of said combination then show the by adding simulated background and signal. The resulting coefficients of said combination then show the expected signal
expected signal rate for the simulated cross section. An example of such a fit can be seen in [@fig:cb_fit]. In this rate for the simulated signal cross section of $\SI{1}{\pico\barn}$. An example of such a fit can be seen in
figure, a binning of 200 GeV is used for presentational purposes. The analysis itself is conducted using a 1 GeV [@fig:cb_fit]. In this figure, a binning of 200 GeV is used for presentational purposes. The analysis itself is
binning. It can be seen that the fit works very well and therefore confirms the functions chosen to model signal and conducted using a 1 GeV binning. It can be seen that the fit works very well and therefore confirms the functions chosen
background. This is supported by a $\chi^2 /$ ndof of 0.5 and a found mean for the signal at 2999 $\pm$ 23 to model signal and background. This is supported by a $\chi^2 /$ ndof of 0.5 and a found mean for the signal at 2999
$\si{\giga\eV}$ which is in very good agreement with the expected 3000 GeV mean. Those numbers clearly show that the $\pm$ 23 $\si{\giga\eV}$ which is in very good agreement with the expected 3000 GeV mean. Those numbers clearly show
method in use is able to successfully describe the simulated toy data. that the method in use is able to successfully describe the simulated toy data.
![ ![
Combined fit of signal and background on a toy dataset with gaussian errors and a simulated resonance mass of 3 TeV. Combined fit of signal and background on a toy dataset with gaussian errors and a simulated resonance mass of 3 TeV.
@ -573,6 +572,7 @@ events.
\includegraphics{./figures/2016/v1_Njet_N_jets_stack.eps} \includegraphics{./figures/2016/v1_Njet_N_jets_stack.eps}
\end{minipage} \end{minipage}
\begin{minipage}{\textwidth} \begin{minipage}{\textwidth}
\vspace{0.1cm}
\centering\textbf{Comparison for the combined dataset} \centering\textbf{Comparison for the combined dataset}
\end{minipage} \end{minipage}
\begin{minipage}{0.5\textwidth} \begin{minipage}{0.5\textwidth}
@ -592,8 +592,9 @@ two jets with the highest transverse momentum. The q\* particle is expected to b
mass energy of the collision and will therefore be almost stationary. Its decay products should therefore be close to mass energy of the collision and will therefore be almost stationary. Its decay products should therefore be close to
back to back, which means the $\Delta\eta$ distribution is expected to peak at zero. At the same time, particles back to back, which means the $\Delta\eta$ distribution is expected to peak at zero. At the same time, particles
originating from QCD effects are expected to have a higher $\Delta\eta$. To maintain comparability, the same selection originating from QCD effects are expected to have a higher $\Delta\eta$. To maintain comparability, the same selection
as in previous research of $\Delta\eta \le 1.3$ is used. The comparison of the $m_{jj}$ distribution seen in [@fig:deta] as in previous research of $\Delta\eta \le 1.3$ is used. In the top two distributions of [@fig:deta], this cut is marked
before and after the cut clearly shows, that the signal sensitivity was greatly improved by this cut. by a vertical black line. The difference in the $m_{jj}$ distribution shows the strong reduction of the background by
this cut.
\begin{figure} \begin{figure}
\begin{minipage}{\textwidth} \begin{minipage}{\textwidth}
@ -625,7 +626,8 @@ before and after the cut clearly shows, that the signal sensitivity was greatly
\begin{minipage}{0.5\textwidth} \begin{minipage}{0.5\textwidth}
\includegraphics{./figures/combined/v1_Eta_invMass_stack.eps} \includegraphics{./figures/combined/v1_Eta_invMass_stack.eps}
\end{minipage} \end{minipage}
\caption{Demonstration of the effect of the $\Delta\eta$ cut at $\Delta\eta \le 1.3$. \newline \caption{Demonstration of the effect of the $\Delta\eta$ cut at $\Delta\eta \le 1.3$ on the $m_{jj}$ distribution.
\newline
Left: Partial dataset of $\SI{35.92}{\per\femto\barn}$ Right: Full dataset of $\SI{137.19}{\per\femto\barn}$. Left: Partial dataset of $\SI{35.92}{\per\femto\barn}$ Right: Full dataset of $\SI{137.19}{\per\femto\barn}$.
} }
\label{fig:deta} \label{fig:deta}
@ -686,7 +688,7 @@ distributions are in very good agreement.
For analysing the data from the CMS experiment, jet energy corrections have to be applied. Those are to calibrate the For analysing the data from the CMS experiment, jet energy corrections have to be applied. Those are to calibrate the
ECAL and HCAL parts of the CMS, so the energy of the detected particles can be measured correctly. The corrections used ECAL and HCAL parts of the CMS, so the energy of the detected particles can be measured correctly. The corrections used
were published by the CMS group. [cite todo] were recommended by the CMS group for internal use [@JEC].
\begin{figure} \begin{figure}
\begin{minipage}{0.5\textwidth} \begin{minipage}{0.5\textwidth}
@ -718,7 +720,7 @@ were published by the CMS group. [cite todo]
\end{figure} \end{figure}
### Sideband ### Sideband region
The sideband region is introduced to make sure no bias in the data and Monte Carlo simulation is introduced and also to The sideband region is introduced to make sure no bias in the data and Monte Carlo simulation is introduced and also to
verify the agreement of data and simulation. It is a region in which no signal event is expected. Again, data and the verify the agreement of data and simulation. It is a region in which no signal event is expected. Again, data and the
@ -793,7 +795,7 @@ boosted vector boson.
The lower the $\tau_{21}$ is, the more likely a jet is caused by the decay of a vector boson. Therefore a selection will The lower the $\tau_{21}$ is, the more likely a jet is caused by the decay of a vector boson. Therefore a selection will
be introduced, so that $\tau_{21}$ of one candidate jet is smaller then some value that will be determined by the be introduced, so that $\tau_{21}$ of one candidate jet is smaller then some value that will be determined by the
optimization process described in the next chapter. As candidate jet the one of the two highest $p_t$ jets passing the optimisation process described in the next chapter. As candidate jet the one of the two highest $p_t$ jets passing the
soft-drop mass window is used. If both of them pass, the one with higher $p_t$ is chosen. soft-drop mass window is used. If both of them pass, the one with higher $p_t$ is chosen.
## DeepAK8 ## DeepAK8
@ -803,9 +805,8 @@ reduces the background rate by up to a factor of ~10 with the same signal effici
approaches like the N-Subjettiness method. This is shown by [@fig:ak8_eff], showing a comparison of background and approaches like the N-Subjettiness method. This is shown by [@fig:ak8_eff], showing a comparison of background and
signal efficiency of the DeepAK8 tagger with, between others, the $\tau_{20}$ tagger that is also used in this analysis. signal efficiency of the DeepAK8 tagger with, between others, the $\tau_{20}$ tagger that is also used in this analysis.
![Comparison of tagger efficiencies, showing, between others, the DeepAK8-MD (which stands for mass decorrelated and is ![Comparison of tagger efficiencies, showing, between others, the DeepAK8 and $\tau_{21}$ tagger used in this analysis
the one used for this research) and $\tau_{21}$ tagger used in this analysis. [@DEEP_BOOSTED].](./figures/deep_ak8.pdf){#fig:ak8_eff width=60%}
Taken from [@DEEP_BOOSTED]](./figures/deep_ak8.pdf){#fig:ak8_eff width=60%}
The DNN has two input lists for each jet. The first is a list of up to 100 constituent particles of the jet, sorted by The DNN has two input lists for each jet. The first is a list of up to 100 constituent particles of the jet, sorted by
decreasing $p_t$. A total of 42 properties of the particles such es $p_t$, energy deposit, charge and the decreasing $p_t$. A total of 42 properties of the particles such es $p_t$, energy deposit, charge and the
@ -816,32 +817,32 @@ networks (CNN) that each process one of the input lists. The outputs of the two
a fully-connected network to identify the jet. The network was trained with a sample of 40 million jets, another 10 a fully-connected network to identify the jet. The network was trained with a sample of 40 million jets, another 10
million jets were used for development and validation. million jets were used for development and validation.
In this thesis, the mass decorrelated version of the DeepAK8 tagger is used. It adds an additional mass predictor layer, In this thesis, the mass decorrelated version of the DeepAK8 tagger, called DeepAK8-MD but further referred to as only
that is trained to quantify how strongly the output of the non-decorrelated tagger is correlated to the mass of a DeepAK8, is used. It adds an additional mass predictor layer, that is trained to quantify how strongly the output of the
particle. Its output is fed back to the network as a penalty so it avoids using features of the particles correlated to non-decorrelated tagger is correlated to the mass of a particle. Its output is fed back to the network as a penalty so
their mass. The result is a largely mass decorrelated tagger of heavy resonances, that doesn't introduce a bias in the it avoids using features of the particles correlated to their mass. The result is a largely mass decorrelated tagger of
jet mass shape. As can be seen in [@fig:ak8_eff], it performs not as good as the non-mass-decorrelated version, but heavy resonances, that doesn't introduce a bias in the jet mass shape. As can be seen in [@fig:ak8_eff], it performs not
still better than the other taggers it was compared to. as good as the non-mass-decorrelated version, but still better than the other taggers it was compared to.
The higher the discriminant value, called WvsQCD resp. ZvsQCD, of the deep boosted tagger, the more likely is the jet to The higher the discriminant value, called WvsQCD resp. ZvsQCD (further referred to as only VvsQCD), of the DeepAK8
be caused by the decay of a vector boson. Therefore, using the same way to choose a candidate jet as for the tagger, the more likely is the jet to be caused by the decay of a vector boson. Therefore, using the same way to choose
N-subjettiness tagger, a selection is applied so that this candidate jet has a WvsQCD/ZvsQCD value greater than some a candidate jet as for the N-subjettiness tagger, a selection is applied so that this candidate jet has a VvsQCD value
value determined by the optimization presented next. greater than some value determined by the optimisation presented next.
## Optimization {#sec:opt} ## Optimisation {#sec:opt}
To figure out the best value to cut on the discriminants introduced by the two taggers, a value to quantify how good a To figure out the best value to cut on the discriminants introduced by the two taggers, a value to quantify how good a
cut is has to be introduced. For that, the significance calculated by $\frac{S}{\sqrt{B}}$ will be used. S stands for cut is has to be introduced. For that, the significance calculated by $\frac{S}{\sqrt{B}}$ will be used. S stands for
the amount of signal events and B for the amount of background events in a given interval. This value assumes a gaussian the amount of signal events and B for the amount of background events in a given interval. This value assumes a gaussian
error on the background so it will be calculated for the 2 TeV masspoint where enough background events exist to justify error on the background so it will be calculated for the 2 TeV masspoint of the decay to qW where enough background
this assumption. It follows from the central limit theorem that states, that for identical distributed random variables, events exist to justify this assumption, which follows from the central limit theorem [@CLT] that states, that for
their sum converges to a gaussian distribution. The significance therefore represents how good the signal can be identical distributed random variables, their sum converges to a gaussian distribution. The significance represents how
distinguished from the background in units of the standard deviation of the background. As interval, a 10 % margin good the signal can be distinguished from the background in units of the standard deviation of the background. As
around the resonance nominal mass is chosen. The significance is then calculated for different selections on the interval, a 10 % margin around the resonance nominal mass is chosen. The significance is then calculated for different
discriminant of the two taggers and then plotted in dependence on the minimum resp. maximum allowed value of the selections on the discriminant of the two taggers and then plotted in dependence on the minimum resp. maximum allowed
discriminant to pass the selection for the deep boosted resp. the N-subjettiness tagger. value of the discriminant to pass the selection for the DeepAK8 resp. the N-subjettiness tagger.
The optimization process is done using only the data from year 2018, assuming the taggers have similar performances on The optimisation process is done using only the data from year 2018, assuming the taggers have similar performances on
the data of the different years. the data of the different years.
\begin{figure} \begin{figure}
@ -851,28 +852,27 @@ the data of the different years.
\begin{minipage}{0.5\textwidth} \begin{minipage}{0.5\textwidth}
\includegraphics{./figures/sig-tau.pdf} \includegraphics{./figures/sig-tau.pdf}
\end{minipage} \end{minipage}
\caption{Significance plots for the deep boosted (left) and N-subjettiness (right) tagger at the 2 TeV masspoint.} \caption{Significance plots for the DeepAK8 (left) and N-subjettiness (right) tagger at the 2 TeV masspoint.}
\label{fig:sig} \label{fig:sig}
\end{figure} \end{figure}
As a result, the $\tau_{21}$ cut is placed at $\le 0.35$, confirming the value previous research chose and the deep As a result, the $\tau_{21}$ cut is placed at $\le 0.35$, confirming the value previous research chose and the deep
boosted cut is placed at $\ge 0.95$. For the deep boosted tagger, 0.97 would give a slightly higher significance but as boosted cut is placed at $\ge 0.95$. For the DeepAK8 tagger, 0.97 would give a slightly higher significance but as it is
it is very close to the edge where the significance drops very low and the higher the cut the less background will be very close to the edge where the significance drops very low and the higher the cut the less background will be left to
left to calculate the cross section limits, especially at higher resonance masses, the slightly less strict cut is calculate the cross section limits, especially at higher resonance masses, the slightly less strict cut is chosen.
chosen.
For both taggers also a low purity category is introduced for high TeV regions. Using the cuts optimized for 2 TeV, For both taggers also a low purity category is introduced. Using the cuts optimized for 2 TeV, there are very few
there are very few background events left for higher resonance masses, but to reliably calculate cross section limits, background events left for higher resonance masses, but to reliably calculate cross section limits, those are needed.
those are needed. Therefore in the final cross section calculation, the two categories are combined to have a high Therefore in the final cross section calculation, the two categories are combined to have a high signal sensitivity for
signal sensitivity for all masspoints between 1.6 TeV and 7 TeV that were simulated. As low purity category for the all masspoints between 1.6 TeV and 7 TeV that were simulated. As low purity category for the N-subjettiness tagger, a
N-subjettiness tagger, a cut at $0.35 < \tau_{21} < 0.75$ is used. For the deep boosted tagger the opposite cut from the cut at $0.35 < \tau_{21} < 0.75$ is used. For the DeepAK8 tagger the opposite cut from the high purity category is used:
high purity category is used: $VvsQCD < 0.95$. $VvsQCD < 0.95$.
\newpage \newpage
# Signal extraction {#sec:extr} # Signal extraction {#sec:extr}
After the optimization, now the optimal selection for the N-subjettiness as well as the deep boosted tagger is found and After the optimisation, now the optimal selection for the N-subjettiness as well as the DeepAK8 tagger is found and
applied to the simulated samples as well as the data collected by the CMS experiment. The fit described in [@sec:moa] is applied to the simulated samples as well as the data collected by the CMS experiment. The fit described in [@sec:moa] is
performed for all masspoints of the decay to qW and qZ and for the partial dataset of $\SI{35.92}{\per\femto\barn}$ as performed for all masspoints of the decay to qW and qZ and for the partial dataset of $\SI{35.92}{\per\femto\barn}$ as
well as the complete dataset of $\SI{137.19}{\per\femto\barn}$ separately. well as the complete dataset of $\SI{137.19}{\per\femto\barn}$ separately.
@ -886,11 +886,11 @@ level of 95 %.
In the absence of the q\* particle in the data, the observed limits lie within the $2\sigma$ environment, meaning a 95 % In the absence of the q\* particle in the data, the observed limits lie within the $2\sigma$ environment, meaning a 95 %
confidence level, of the expected limit. This observed limit is plotted together with a theory line, representing the confidence level, of the expected limit. This observed limit is plotted together with a theory line, representing the
cross section limits expected, if the q\* predicted by [@QSTAR_THEORY] would exist. cross section limits expected, if the q\* predicted by [@QSTAR_THEORY] would exist. Since no significant deviation from
Since no significant deviation from the Standard Model is found while looking for the resonance, the crossing of the the Standard Model is found while looking for the resonance, the crossing of the theory line with the observed limit is
theory line with the observed limit is calculated, to have a limit of mass up to which the existence of the q\* particle calculated, to have a limit of mass up to which the existence of the q\* particle can be excluded. To find the
can be excluded. To find the uncertainty of this result, the crossing of the theory line plus, respectively minus, its uncertainty of this result, the crossing of the theory line plus, respectively minus, its uncertainty with the observed
uncertainty with the observed limit is also calculated. limit is also calculated.
## Systematic Uncertainties ## Systematic Uncertainties
@ -910,7 +910,7 @@ analysis and is therefore also considered as an uncertainty, which is approximat
Third, the uncertainty of the parameters of the background fit is also considered, as it might change the background Third, the uncertainty of the parameters of the background fit is also considered, as it might change the background
shape a little and therefore influence how many signal and background events are reconstructed from the data. shape a little and therefore influence how many signal and background events are reconstructed from the data.
Fourth, the uncertainty on the luminosity influence the normalization of the processes. Its value is 2.5 % [cite todo]. Fourth, the uncertainty on the luminosity influences the normalization of the processes. Its value is 2.5 % [@LUMI_UNC].
\newpage \newpage
# Results # Results
@ -927,23 +927,23 @@ seen in [@fig:res2016] were obtained.
As described in [@sec:extr], the calculated cross section limits are used to then calculate a mass limit, meaning the As described in [@sec:extr], the calculated cross section limits are used to then calculate a mass limit, meaning the
lowest possible mass of the q\* particle, by finding the crossing of the theory line with the observed cross section lowest possible mass of the q\* particle, by finding the crossing of the theory line with the observed cross section
limit. In [@fig:res2016dw,@fig:res2016dz] it can be seen, that the observed limit using the deep boosted tagger in the limit. In [@fig:res2016dw,@fig:res2016dz] it can be seen, that the observed limit using the DeepAK8 tagger in the region
region where theory and observed limit cross is very high compared to when using the N-subjettiness tagger. Therefore where theory and observed limit cross is very high compared to when using the N-subjettiness tagger. Therefore the two
the two lines cross at lower resonance masses, which results in lower exclusion limits on the mass of the q\* particle lines cross at lower resonance masses, which results in lower exclusion limits on the mass of the q\* particle causing
causing the deep boosted tagger to perform worse than the N-subjettiness tagger in regards of establishing those limits the DeepAK8 tagger to perform worse than the N-subjettiness tagger in regards of establishing those limits as can be
as can be seen in [@tbl:res2016]. The table also shows the upper and lower limits on the mass found by calculating the seen in [@tbl:res2016]. The table also shows the upper and lower limits on the mass found by calculating the crossing of
crossing of the theory plus resp. minus its uncertainty. Due to the theory and the observed limits line being slowly the theory plus resp. minus its uncertainty. Due to the theory and the observed limits line being slowly falling in the
falling in the high TeV region, even a small uncertainty of the theory can cause a high difference of the mass limit. high TeV region, even a small uncertainty of the theory can cause a high difference of the mass limit.
: Mass limits found using the partial dataset of $\SI{35.92}{\per\femto\barn}$ {#tbl:res2016} : Mass limits found using the partial dataset of $\SI{35.92}{\per\femto\barn}$ {#tbl:res2016}
| Decay | Tagger | Limit [TeV] | Upper Limit [TeV] | Lower Limit [TeV] | | Decay | Tagger | Limit [TeV] | Upper Limit [TeV] | Lower Limit [TeV] |
|-------|--------------|-------------|-------------------|-------------------| |-------|-------------|-------------|-------------------|-------------------|
| qW | $\tau_{21}$ | 5.39 | 6.01 | 4.99 | | qW | $\tau_{21}$ | 5.39 | 6.01 | 4.99 |
| qW | deep boosted | 4.96 | 5.19 | 4.84 | | qW | DeepAK8 | 4.96 | 5.19 | 4.84 |
| qZ | $\tau_{21}$ | 4.86 | 4.96 | 4.70 | | qZ | $\tau_{21}$ | 4.86 | 4.96 | 4.70 |
| qZ | deep boosted | 4.62 | 4.71 | 4.49 | | qZ | DeepAK8 | 4.62 | 4.71 | 4.49 |
\begin{figure}% \begin{figure}%
@ -951,13 +951,13 @@ falling in the high TeV region, even a small uncertainty of the theory can cause
\subfloat[Decay to qW, using N-subjettiness tagger]{% \subfloat[Decay to qW, using N-subjettiness tagger]{%
\label{fig:res2016tw}% \label{fig:res2016tw}%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_2016tau_13TeV.pdf}} \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_2016tau_13TeV.pdf}}
\subfloat[Decay to qW, using deep boosted tagger]{% \subfloat[Decay to qW, using DeepAK8 tagger]{%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_2016db_13TeV.pdf}% \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_2016db_13TeV.pdf}%
\label{fig:res2016dw}}\\ \label{fig:res2016dw}}\\
\subfloat[Decay to qZ, using N-subjettiness tagger]{% \subfloat[Decay to qZ, using N-subjettiness tagger]{%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_2016tau_13TeV.pdf}% \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_2016tau_13TeV.pdf}%
\label{fig:res2016tz}}% \label{fig:res2016tz}}%
\subfloat[Decay to qZ, using deep boosted tagger]{% \subfloat[Decay to qZ, using DeepAK8 tagger]{%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_2016db_13TeV.pdf}% \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_2016db_13TeV.pdf}%
\label{fig:res2016dz}}% \label{fig:res2016dz}}%
\caption{Results of the cross section limits for the partial dataset of 2016 using the $\tau_{21}$ tagger and the deep \caption{Results of the cross section limits for the partial dataset of 2016 using the $\tau_{21}$ tagger and the deep
@ -987,8 +987,8 @@ results are in good agreement. The cross section limits of the previous research
\begin{minipage}{0.5\textwidth} \begin{minipage}{0.5\textwidth}
\includegraphics{./figures/results/prev_qZ.png} \includegraphics{./figures/results/prev_qZ.png}
\end{minipage} \end{minipage}
\caption{Previous results of the cross section limits for q\* decaying to qW (left) and q\* decaying to qZ (right). \caption{Previous results of the cross section limits for q\* decaying to qW (left) and q\* decaying to qZ (right)
Taken from \cite{PREV_RESEARCH}.} \cite{PREV_RESEARCH}.}
\label{fig:prev} \label{fig:prev}
\end{figure} \end{figure}
@ -1003,12 +1003,12 @@ The results for the mass limits of the combined years are presented in the follo
: Mass limits found using $\SI{137.19}{\per\femto\barn}$ of data {#tbl:resCombined} : Mass limits found using $\SI{137.19}{\per\femto\barn}$ of data {#tbl:resCombined}
| Decay | Tagger | Limit [TeV] | Upper Limit [TeV] | Lower Limit [TeV] | | Decay | Tagger | Limit [TeV] | Upper Limit [TeV] | Lower Limit [TeV] |
|-------|--------------|-------------|-------------------|-------------------| |-------|-------------|-------------|-------------------|-------------------|
| qW | $\tau_{21}$ | 6.00 | 6.26 | 5.74 | | qW | $\tau_{21}$ | 6.00 | 6.26 | 5.74 |
| qW | deep boosted | 6.11 | 6.31 | 5.39 | | qW | DeepAK8 | 6.11 | 6.31 | 5.39 |
| qZ | $\tau_{21}$ | 5.49 | 5.76 | 5.29 | | qZ | $\tau_{21}$ | 5.49 | 5.76 | 5.29 |
| qZ | deep boosted | 4.95 | 5.13 | 4.85 | | qZ | DeepAK8 | 4.95 | 5.13 | 4.85 |
The combination of the three years not just improved the cross section limits, but also the limit for the mass of the The combination of the three years not just improved the cross section limits, but also the limit for the mass of the
@ -1021,16 +1021,16 @@ what was concluded by the previous research [@PREV_RESEARCH].
\subfloat[Decay to qW, using N-subjettiness tagger]{% \subfloat[Decay to qW, using N-subjettiness tagger]{%
\label{fig:resCombinedtw}% \label{fig:resCombinedtw}%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_Combinedtau_13TeV.pdf}} \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_Combinedtau_13TeV.pdf}}
\subfloat[Decay to qW, using deep boosted tagger]{% \subfloat[Decay to qW, using DeepAK8 tagger]{%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_Combineddb_13TeV.pdf}% \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_Combineddb_13TeV.pdf}%
\label{fig:resCombineddw}}\\ \label{fig:resCombineddw}}\\
\subfloat[Decay to qZ, using N-subjettiness tagger]{% \subfloat[Decay to qZ, using N-subjettiness tagger]{%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_Combinedtau_13TeV.pdf}% \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_Combinedtau_13TeV.pdf}%
\label{fig:resCombinedtz}}% \label{fig:resCombinedtz}}%
\subfloat[Decay to qZ, using deep boosted tagger]{% \subfloat[Decay to qZ, using DeepAK8 tagger]{%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_Combineddb_13TeV.pdf}% \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_Combineddb_13TeV.pdf}%
\label{fig:resCombineddz}}% \label{fig:resCombineddz}}%
\caption{Results of the cross section limits for the combined dataset using the $\tau_{21}$ tagger and the deep boosted \caption{Results of the cross section limits for the combined dataset using the $\tau_{21}$ tagger and the DeepAK8
tagger.} tagger.}
\label{fig:resCombined} \label{fig:resCombined}
\end{figure} \end{figure}
@ -1038,25 +1038,26 @@ tagger.}
## Comparison of taggers ## Comparison of taggers
The results presented in [@tbl:res2016, @tbl:resCombined] show, that the deep boosted tagger was not able to The results presented in [@tbl:res2016, @tbl:resCombined] show, that the DeepAK8 tagger was not able to
significantly improve the results compared to the N-subjettiness tagger. significantly improve the results compared to the N-subjettiness tagger.
For further comparison, in [@fig:limit_comp] the expected limits of the different taggers for the q\* $\rightarrow$ qW For further comparison, in [@fig:limit_comp] the expected limits of the different taggers for the q\* $\rightarrow$ qW
and the q\* $\rightarrow$ qZ decay are shown. It can be seen, that the deep boosted is at best as good as the and the q\* $\rightarrow$ qZ decay are shown. It can be seen, that the DeepAK8 is at best as good as the
N-subjettiness tagger. This was not the expected result, as the deep neural network was already found to provide a N-subjettiness tagger. This was not the expected result, as the deep neural network was already found to provide a
higher significance in the optimisation done in [@sec:opt]. The higher significance should also result in lower cross higher significance in the optimisation done in [@sec:opt]. The higher significance should also result in lower cross
section limits. To make sure, there is no mistake in the setup, also the expected cross section limits using only the section limits. To make sure, there is no mistake in the setup, also the expected cross section limits using only the
high purity category of the two taggers with 2018 data are compared in [@fig:comp_2018]. There, the cross section limits high purity category of the two taggers with 2018 data are compared in [@fig:comp_2018]. There, the cross section limits
calculated using the deep boosted tagger are a bit lower than with the N-subjettiness tagger, showing, that the method calculated using the DeepAK8 tagger are a bit lower than with the N-subjettiness tagger, showing, that the method
used for optimisation is working but the assumption of it also applying to the combined dataset did not hold. used for optimisation is working but the assumption of it also applying to the combined dataset did not hold.
This can be explained by some training issues identified lately. This can be explained by some training issues identified lately.
The training of the DeepAK8 tagger was done for the data of year 2016. It therefore performs differently for the data of The training of the DeepAK8 tagger was done for the data of year 2016. It therefore performs differently for the data of
the other years. This caused the DeepAK8 tagger to perform significantly worse than it could have for several reasons. the other years. This caused the DeepAK8 tagger to perform significantly worse than it could have for several reasons.
First, the optimization done for the data of year 2018 could therefore not be applied to the other datasets. Second, First, the optimisation done for the data of year 2018 could therefore not be applied to the other datasets. Second,
even for the data of 2016, a newer version of the background simulation was used, that, in combination with the samples even for the data of 2016, a newer version of the background simulation was used, that, in combination with the samples
used for the signal, turned out to be the worst case scenario for the used training. used for the signal, turned out to be the worst case scenario for the used training.
Recently, the training was improved to better perform across all datasets, but those changes could not be incorporated Recently, the training was improved to better perform across all datasets, but those changes could not be incorporated
into this thesis due to it not being possible to do this in a reasonable timeframe. into this thesis due to it not being possible to do this in a reasonable timeframe.
\newpage
@ -1073,8 +1074,8 @@ decay to qZ}
\end{figure} \end{figure}
![Comparision of deep boosted and N-subjettiness tagger in the high purity category using the data from year 2018. ![Comparison of DeepAK8 and N-subjettiness tagger in the high purity category using the data from year 2018.
](./figures/limit_comp_2018.pdf){#fig:comp_2018 width=70%} ](./figures/limit_comp_2018.pdf){#fig:comp_2018 width=55%}
\clearpage \clearpage
\newpage \newpage
@ -1085,7 +1086,7 @@ In this thesis, a search for the q\* particle decaying to q + W and q + Z was pr
collisions at the LHC of an integrated luminosity of $\SI{137.19}{\per\femto\barn}$ collected by the CMS experiment at a collisions at the LHC of an integrated luminosity of $\SI{137.19}{\per\femto\barn}$ collected by the CMS experiment at a
centre-of-mass energy of $\sqrt{s} = \SI{13}{\tera\eV}$ has been searched. Also a partial dataset of centre-of-mass energy of $\sqrt{s} = \SI{13}{\tera\eV}$ has been searched. Also a partial dataset of
$\SI{35.92}{\per\femto\barn}$ was analysed, to be able to compare the results to previous research. Monte Carlo $\SI{35.92}{\per\femto\barn}$ was analysed, to be able to compare the results to previous research. Monte Carlo
simulations were used to estimate the QCD background and signal. simulations were used to model the QCD multijet background and signal shapes.
A selection was introduced to reduce background events and enhance signal sensitivity. This selection required at least A selection was introduced to reduce background events and enhance signal sensitivity. This selection required at least
two jets, a $\Delta\eta \ge 1.3$ between the two highest $p_t$ jets, an invariant mass of the two highest $p_t$ jets two jets, a $\Delta\eta \ge 1.3$ between the two highest $p_t$ jets, an invariant mass of the two highest $p_t$ jets
@ -1097,7 +1098,7 @@ vector boson. For both of them, two categories were introduced. A high purity ca
sensitivity in the low TeV region of the invariant mass spectrum and a low purity category, aiming for better statistics sensitivity in the low TeV region of the invariant mass spectrum and a low purity category, aiming for better statistics
in the high TeV region. For the DeepAK8 tagger, a high purity category of $VvsQCD > 0.95$ and a low purity category of in the high TeV region. For the DeepAK8 tagger, a high purity category of $VvsQCD > 0.95$ and a low purity category of
$VvsQCD \le 0.95$ was used. For the N-subjettiness tagger the high purity category was $\tau_{21} < 0.35$ and the low $VvsQCD \le 0.95$ was used. For the N-subjettiness tagger the high purity category was $\tau_{21} < 0.35$ and the low
purity category $0.35 < \tau_{21} < 0.75$. These values were obtained by optimizing for the highest possible purity category $0.35 < \tau_{21} < 0.75$. These values were obtained by optimising for the highest possible
significance of the signal. significance of the signal.
A combined fit to the dijet invariant mass distribution of background plus signal has been used to determine their shape A combined fit to the dijet invariant mass distribution of background plus signal has been used to determine their shape
@ -1106,8 +1107,12 @@ Because no significant deviation from the Standard Model was observed, new exclu
particle were set. These are 6.1 TeV by analyzing the decay to qW, respectively 5.5 TeV for the decay to qZ. Those particle were set. These are 6.1 TeV by analyzing the decay to qW, respectively 5.5 TeV for the decay to qZ. Those
limits are about 1 TeV higher than the ones found in previous research, that found them to be 5 TeV resp. 4.7 TeV. limits are about 1 TeV higher than the ones found in previous research, that found them to be 5 TeV resp. 4.7 TeV.
The DeepAK8 tagger performed worse than was expected. This can be explained with some training issues identified lately. The performance of the two taggers used have been compared and found to produce similar results. This was unexpected, as
Therefore, with an updated training, it is expected that the presented results can be further improved. the DeepAK8 tagger was supposed to perform better than the N-subjettiness tagger. The result analysing the decay to qW
was $\SI{0.1}{\tera\eV}$ better using the DeepAK8 than with the N-subjettiness tagger, but analysing the decay to qZ it
was $\SI{0.5}{\tera\eV}$ worse. The performance of the DeepAK8 tagger is likely to significantly improve with an updated
training that was not yet available in the framework used by this thesis.
\newpage \newpage

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@ -41,7 +41,7 @@
> >
]> ]>
<requests version="1.0"> <requests version="1.0">
<internal package="biblatex" priority="9" active="0"> <internal package="biblatex" priority="9" active="1">
<generic>latex</generic> <generic>latex</generic>
<provides type="dynamic"> <provides type="dynamic">
<file>thesis.bcf</file> <file>thesis.bcf</file>

470
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@ -78,7 +78,7 @@
\usepackage{csquotes} \usepackage{csquotes}
\usepackage{abstract} \usepackage{abstract}
\pagenumbering{gobble} \pagenumbering{gobble}
\setlength{\parskip}{0.5em} \setlength{\parskip}{0.4em}
\bibliographystyle{lucas_unsrt} \bibliographystyle{lucas_unsrt}
\makeatletter \makeatletter
\@ifpackageloaded{subfig}{}{\usepackage{subfig}} \@ifpackageloaded{subfig}{}{\usepackage{subfig}}
@ -168,7 +168,7 @@ to a mass of 6.1\ TeV (qW) resp. 5.5\ TeV (qZ) with a confidence level of 95 \%.
the limits found by a previous research of data with an integrated luminosity of $\SI{35.92}{\per\femto\barn}$ collected the limits found by a previous research of data with an integrated luminosity of $\SI{35.92}{\per\femto\barn}$ collected
by the CMS experiment in 2016, excluding the q* particle up to a mass of 5.0\ TeV resp. 4.7\ TeV. The DeepAK8 tagger is by the CMS experiment in 2016, excluding the q* particle up to a mass of 5.0\ TeV resp. 4.7\ TeV. The DeepAK8 tagger is
found to currently be at the same level as the N-subjettiness tagger, giving a $\SI{0.1}{\tera\eV}$ better result for found to currently be at the same level as the N-subjettiness tagger, giving a $\SI{0.1}{\tera\eV}$ better result for
the decay to qW but a by $\SI{0.6}{\tera\eV}$ worse one for the decay to qZ. By optimizing the neural network's training the decay to qW but a by $\SI{0.5}{\tera\eV}$ worse one for the decay to qZ. By optimising the neural network's training
for the datasets of 2016, 2017 and 2018, the sensitivity can likely be improved. for the datasets of 2016, 2017 and 2018, the sensitivity can likely be improved.
\end{abstract} \end{abstract}
@ -188,7 +188,7 @@ Tagger verglichen. Im Ergebnis kann keine signifikante Abweichung vom Standardmo
wird mit einem Konfidenzniveau von 95 \% bis zu einer Masse von 6.1\ TeV (qW) bzw. 5.5\ TeV (qZ) ausgeschlossen. Das Limit wird mit einem Konfidenzniveau von 95 \% bis zu einer Masse von 6.1\ TeV (qW) bzw. 5.5\ TeV (qZ) ausgeschlossen. Das Limit
liegt etwa 1\ TeV höher, als das anhand des $\SI{35.92}{\per\femto\barn}$ großen Datensatzes von 2016 gefundene von 5.0 liegt etwa 1\ TeV höher, als das anhand des $\SI{35.92}{\per\femto\barn}$ großen Datensatzes von 2016 gefundene von 5.0
TeV bzw. 4.7\ TeV. Beim Zerfall zu qW erzielt der DeepAK8 Tagger ein um $\SI{0.1}{\tera\eV}$ besseres Ergebnis, als der TeV bzw. 4.7\ TeV. Beim Zerfall zu qW erzielt der DeepAK8 Tagger ein um $\SI{0.1}{\tera\eV}$ besseres Ergebnis, als der
N-Subjettiness Tagger, beim Zerfall zu qZ jedoch ein um $\SI{0.6}{\tera\eV}$ schlechteres. Durch Verbesserung des N-Subjettiness Tagger, beim Zerfall zu qZ jedoch ein um $\SI{0.5}{\tera\eV}$ schlechteres. Durch Verbesserung des
Trainings des neuronalen Netzwerkes für die drei Datensätze von 2016, 2017 und 2018, gibt es aber noch Potential die Trainings des neuronalen Netzwerkes für die drei Datensätze von 2016, 2017 und 2018, gibt es aber noch Potential die
Sensitivität zu verbessern. Sensitivität zu verbessern.
@ -278,19 +278,18 @@ and leptons. Quarks and leptons can also be categorized into three
generations, each of which contains two particles, also called flavours. generations, each of which contains two particles, also called flavours.
For leptons, the three generations each consist of a charged lepton and For leptons, the three generations each consist of a charged lepton and
its corresponding neutrino, namely the electron, the muon and the tau. its corresponding neutrino, namely the electron, the muon and the tau.
The three quark generations consist of first, the up and down, second, The three quark generations consist of the up and down, the charm and
the charm and strange, and third, the top and bottom quark. A full list strange, and the top and bottom quark. A full list of particles of the
of particles of the standard model can be found in Fig.~\ref{fig:sm}. standard model can be found in Fig.~\ref{fig:sm}. Furthermore, all
Furthermore, all fermions have an associated anti particle with reversed fermions have an associated anti particle with reversed charge. Bound
charge. Bound states of multiple quarks also exist and are called states of multiple quarks also exist and are called hadrons.
hadrons.
\begin{figure} \begin{figure}
\hypertarget{fig:sm}{% \hypertarget{fig:sm}{%
\centering \centering
\includegraphics[width=0.5\textwidth,height=\textheight]{./figures/sm_wikipedia.pdf} \includegraphics[width=0.5\textwidth,height=\textheight]{./figures/sm_wikipedia.pdf}
\caption{Elementary particles of the Standard Model and their mass \caption{Elementary particles of the Standard Model and their mass
charge and spin. Taken from \autocite{SM}}\label{fig:sm} charge and spin \autocite{SM}.}\label{fig:sm}
} }
\end{figure} \end{figure}
@ -563,11 +562,11 @@ field of 3.8 T. Outside of the solenoid are big muon chambers. In 2016
the CMS captured data of an integrated luminosity of the CMS captured data of an integrated luminosity of
\(\SI{37.80}{\per\femto\barn}\). In 2017 it collected \(\SI{37.80}{\per\femto\barn}\). In 2017 it collected
\(\SI{44.98}{\per\femto\barn}\) and in 2018 \(\SI{44.98}{\per\femto\barn}\) and in 2018
\(\SI{63.67}{\per\femto\barn}\). Because of eventual inconsistencies in \(\SI{63.67}{\per\femto\barn}\) \autocite{CMS_LUMI}. The amount of data
the setup, some data have to be discarded. The amount of usable data is usable for research is \(\SI{35.92}{\per\femto\barn}\),
\(\SI{34.92}{\per\femto\barn}\), \(\SI{41.53}{\per\femto\barn}\) and \(\SI{41.53}{\per\femto\barn}\) and \(\SI{59.74}{\per\femto\barn}\) for
\(\SI{59.74}{\per\femto\barn}\) for the years 2016, 2017 and 2018, the years 2016, 2017 and 2018, totalling to
totalling to \(\SI{137.19}{\per\femto\barn}\) of data. \(\SI{137.19}{\per\femto\barn}\) of data.
\begin{figure} \begin{figure}
\hypertarget{fig:cms_setup}{% \hypertarget{fig:cms_setup}{%
@ -603,8 +602,8 @@ neutrinos.
\centering \centering
\includegraphics[width=0.6\textwidth,height=\textheight]{./figures/cms_coordinates.png} \includegraphics[width=0.6\textwidth,height=\textheight]{./figures/cms_coordinates.png}
\caption{Coordinate conventions of the CMS illustrating the use of \caption{Coordinate conventions of the CMS illustrating the use of
\(\eta\) and \(\phi\). The Z axis is in beam direction. Taken from \(\eta\) and \(\phi\). The Z axis is in beam direction
\autocite{COORD_PLOT}}\label{fig:cmscoords} \autocite{COORD_PLOT}.}\label{fig:cmscoords}
} }
\end{figure} \end{figure}
@ -721,8 +720,8 @@ For this analysis, a radius of 0.8 is used.
\includegraphics{./figures/antikt-comparision.png} \includegraphics{./figures/antikt-comparision.png}
\caption{Comparison of the \(k_t\), Cambridge/Aachen, SISCone and \caption{Comparison of the \(k_t\), Cambridge/Aachen, SISCone and
anti-\(k_t\) algorithms clustering a sample parton-level event with many anti-\(k_t\) algorithms clustering a sample parton-level event with many
random soft \enquote{ghosts}. Taken from random soft \enquote{ghosts}
\autocite{ANTIKT}}\label{fig:antiktcomparison} \autocite{ANTIKT}.}\label{fig:antiktcomparison}
} }
\end{figure} \end{figure}
@ -789,16 +788,16 @@ background processes, the different particle interactions that take
place in a proton - proton collision are simulated using the place in a proton - proton collision are simulated using the
probabilities provided by the Standard Model by calculating the cross probabilities provided by the Standard Model by calculating the cross
sections of the different Feynman diagrams. This was done using MadGraph sections of the different Feynman diagrams. This was done using MadGraph
and Pythia 8. Later on, also detector effects (like its limited \autocite{MADGRAPH} and Pythia 8 \autocite{PYTHIA8}. Later on, also
resolution) are applied to make sure, they look like real data coming detector effects (like its limited resolution) are applied to make sure,
from the CMS detector. they look like real data coming from the CMS detector.
The q* signal samples are simulated by the probabilities given by the q* The q* signal samples are simulated by the probabilities given by the q*
theory \autocite{QSTAR_THEORY} and assuming a cross section of theory \autocite{QSTAR_THEORY} and assuming a cross section of
\(\SI{1}{\per\pico\barn}\). The simulation was done using MadGraph for \(\SI{1}{\pico\barn}\). The simulation was done using MadGraph
eleven masspoints between 1.6 TeV and 7 TeV. Because of the expected \autocite{MADGRAPH} for eleven masspoints between 1.6 TeV and 7 TeV.
high mass, the signal width will be dominated by the resolution of the Because of the expected high mass, the signal width will be dominated by
detector, not by the natural resonance width. the resolution of the detector, not by the natural resonance width.
The dijet invariant mass distribution of the QCD background is expected The dijet invariant mass distribution of the QCD background is expected
to smoothly fall with higher masses. It is therefore fitted using the to smoothly fall with higher masses. It is therefore fitted using the
@ -830,18 +829,19 @@ not able to reproduce the signal shape as they couldn't model the tails
on both sides of the peak. on both sides of the peak.
A linear combination of the signal and background function is then A linear combination of the signal and background function is then
fitted to a toy dataset with gaussian errors and a simulated signal fitted to a toy dataset with gaussian errors obtained by adding
cross section of \(\SI{1}{\per\pico\barn}\). The resulting coefficients simulated background and signal. The resulting coefficients of said
of said combination then show the expected signal rate for the simulated combination then show the expected signal rate for the simulated signal
cross section. An example of such a fit can be seen in cross section of \(\SI{1}{\pico\barn}\). An example of such a fit can be
Fig.~\ref{fig:cb_fit}. In this figure, a binning of 200 GeV is used for seen in Fig.~\ref{fig:cb_fit}. In this figure, a binning of 200 GeV is
presentational purposes. The analysis itself is conducted using a 1 GeV used for presentational purposes. The analysis itself is conducted using
binning. It can be seen that the fit works very well and therefore a 1 GeV binning. It can be seen that the fit works very well and
confirms the functions chosen to model signal and background. This is therefore confirms the functions chosen to model signal and background.
supported by a \(\chi^2 /\) ndof of 0.5 and a found mean for the signal This is supported by a \(\chi^2 /\) ndof of 0.5 and a found mean for the
at 2999 \(\pm\) 23 \(\si{\giga\eV}\) which is in very good agreement signal at 2999 \(\pm\) 23 \(\si{\giga\eV}\) which is in very good
with the expected 3000 GeV mean. Those numbers clearly show that the agreement with the expected 3000 GeV mean. Those numbers clearly show
method in use is able to successfully describe the simulated toy data. that the method in use is able to successfully describe the simulated
toy data.
\begin{figure} \begin{figure}
\hypertarget{fig:cb_fit}{% \hypertarget{fig:cb_fit}{%
@ -909,6 +909,7 @@ events.
\includegraphics{./figures/2016/v1_Njet_N_jets_stack.eps} \includegraphics{./figures/2016/v1_Njet_N_jets_stack.eps}
\end{minipage} \end{minipage}
\begin{minipage}{\textwidth} \begin{minipage}{\textwidth}
\vspace{0.1cm}
\centering\textbf{Comparison for the combined dataset} \centering\textbf{Comparison for the combined dataset}
\end{minipage} \end{minipage}
\begin{minipage}{0.5\textwidth} \begin{minipage}{0.5\textwidth}
@ -932,10 +933,10 @@ close to back to back, which means the \(\Delta\eta\) distribution is
expected to peak at zero. At the same time, particles originating from expected to peak at zero. At the same time, particles originating from
QCD effects are expected to have a higher \(\Delta\eta\). To maintain QCD effects are expected to have a higher \(\Delta\eta\). To maintain
comparability, the same selection as in previous research of comparability, the same selection as in previous research of
\(\Delta\eta \le 1.3\) is used. The comparison of the \(m_{jj}\) \(\Delta\eta \le 1.3\) is used. In the top two distributions of
distribution seen in Fig.~\ref{fig:deta} before and after the cut Fig.~\ref{fig:deta}, this cut is marked by a vertical black line. The
clearly shows, that the signal sensitivity was greatly improved by this difference in the \(m_{jj}\) distribution shows the strong reduction of
cut. the background by this cut.
\begin{figure} \begin{figure}
\begin{minipage}{\textwidth} \begin{minipage}{\textwidth}
@ -967,7 +968,8 @@ cut.
\begin{minipage}{0.5\textwidth} \begin{minipage}{0.5\textwidth}
\includegraphics{./figures/combined/v1_Eta_invMass_stack.eps} \includegraphics{./figures/combined/v1_Eta_invMass_stack.eps}
\end{minipage} \end{minipage}
\caption{Demonstration of the effect of the $\Delta\eta$ cut at $\Delta\eta \le 1.3$. \newline \caption{Demonstration of the effect of the $\Delta\eta$ cut at $\Delta\eta \le 1.3$ on the $m_{jj}$ distribution.
\newline
Left: Partial dataset of $\SI{35.92}{\per\femto\barn}$ Right: Full dataset of $\SI{137.19}{\per\femto\barn}$. Left: Partial dataset of $\SI{35.92}{\per\femto\barn}$ Right: Full dataset of $\SI{137.19}{\per\femto\barn}$.
} }
\label{fig:deta} \label{fig:deta}
@ -1043,8 +1045,8 @@ good agreement.
For analysing the data from the CMS experiment, jet energy corrections For analysing the data from the CMS experiment, jet energy corrections
have to be applied. Those are to calibrate the ECAL and HCAL parts of have to be applied. Those are to calibrate the ECAL and HCAL parts of
the CMS, so the energy of the detected particles can be measured the CMS, so the energy of the detected particles can be measured
correctly. The corrections used were published by the CMS group. {[}cite correctly. The corrections used were recommended by the CMS group for
todo{]} internal use \autocite{JEC}.
\begin{figure} \begin{figure}
\begin{minipage}{0.5\textwidth} \begin{minipage}{0.5\textwidth}
@ -1075,8 +1077,8 @@ todo{]}
\label{fig:data-mc} \label{fig:data-mc}
\end{figure} \end{figure}
\hypertarget{sideband}{% \hypertarget{sideband-region}{%
\subsubsection{Sideband}\label{sideband}} \subsubsection{Sideband region}\label{sideband-region}}
The sideband region is introduced to make sure no bias in the data and The sideband region is introduced to make sure no bias in the data and
Monte Carlo simulation is introduced and also to verify the agreement of Monte Carlo simulation is introduced and also to verify the agreement of
@ -1174,7 +1176,7 @@ boson.
The lower the \(\tau_{21}\) is, the more likely a jet is caused by the The lower the \(\tau_{21}\) is, the more likely a jet is caused by the
decay of a vector boson. Therefore a selection will be introduced, so decay of a vector boson. Therefore a selection will be introduced, so
that \(\tau_{21}\) of one candidate jet is smaller then some value that that \(\tau_{21}\) of one candidate jet is smaller then some value that
will be determined by the optimization process described in the next will be determined by the optimisation process described in the next
chapter. As candidate jet the one of the two highest \(p_t\) jets chapter. As candidate jet the one of the two highest \(p_t\) jets
passing the soft-drop mass window is used. If both of them pass, the one passing the soft-drop mass window is used. If both of them pass, the one
with higher \(p_t\) is chosen. with higher \(p_t\) is chosen.
@ -1196,9 +1198,8 @@ analysis.
\centering \centering
\includegraphics[width=0.6\textwidth,height=\textheight]{./figures/deep_ak8.pdf} \includegraphics[width=0.6\textwidth,height=\textheight]{./figures/deep_ak8.pdf}
\caption{Comparison of tagger efficiencies, showing, between others, the \caption{Comparison of tagger efficiencies, showing, between others, the
DeepAK8-MD (which stands for mass decorrelated and is the one used for DeepAK8 and \(\tau_{21}\) tagger used in this analysis
this research) and \(\tau_{21}\) tagger used in this analysis. Taken \autocite{DEEP_BOOSTED}.}\label{fig:ak8_eff}
from \autocite{DEEP_BOOSTED}}\label{fig:ak8_eff}
} }
\end{figure} \end{figure}
@ -1216,26 +1217,27 @@ fully-connected network to identify the jet. The network was trained
with a sample of 40 million jets, another 10 million jets were used for with a sample of 40 million jets, another 10 million jets were used for
development and validation. development and validation.
In this thesis, the mass decorrelated version of the DeepAK8 tagger is In this thesis, the mass decorrelated version of the DeepAK8 tagger,
used. It adds an additional mass predictor layer, that is trained to called DeepAK8-MD but further referred to as only DeepAK8, is used. It
quantify how strongly the output of the non-decorrelated tagger is adds an additional mass predictor layer, that is trained to quantify how
correlated to the mass of a particle. Its output is fed back to the strongly the output of the non-decorrelated tagger is correlated to the
network as a penalty so it avoids using features of the particles mass of a particle. Its output is fed back to the network as a penalty
correlated to their mass. The result is a largely mass decorrelated so it avoids using features of the particles correlated to their mass.
tagger of heavy resonances, that doesn't introduce a bias in the jet The result is a largely mass decorrelated tagger of heavy resonances,
mass shape. As can be seen in Fig.~\ref{fig:ak8_eff}, it performs not as that doesn't introduce a bias in the jet mass shape. As can be seen in
good as the non-mass-decorrelated version, but still better than the Fig.~\ref{fig:ak8_eff}, it performs not as good as the
other taggers it was compared to. non-mass-decorrelated version, but still better than the other taggers
it was compared to.
The higher the discriminant value, called WvsQCD resp. ZvsQCD, of the The higher the discriminant value, called WvsQCD resp. ZvsQCD (further
deep boosted tagger, the more likely is the jet to be caused by the referred to as only VvsQCD), of the DeepAK8 tagger, the more likely is
decay of a vector boson. Therefore, using the same way to choose a the jet to be caused by the decay of a vector boson. Therefore, using
candidate jet as for the N-subjettiness tagger, a selection is applied the same way to choose a candidate jet as for the N-subjettiness tagger,
so that this candidate jet has a WvsQCD/ZvsQCD value greater than some a selection is applied so that this candidate jet has a VvsQCD value
value determined by the optimization presented next. greater than some value determined by the optimisation presented next.
\hypertarget{sec:opt}{% \hypertarget{sec:opt}{%
\subsection{Optimization}\label{sec:opt}} \subsection{Optimisation}\label{sec:opt}}
To figure out the best value to cut on the discriminants introduced by To figure out the best value to cut on the discriminants introduced by
the two taggers, a value to quantify how good a cut is has to be the two taggers, a value to quantify how good a cut is has to be
@ -1243,19 +1245,20 @@ introduced. For that, the significance calculated by
\(\frac{S}{\sqrt{B}}\) will be used. S stands for the amount of signal \(\frac{S}{\sqrt{B}}\) will be used. S stands for the amount of signal
events and B for the amount of background events in a given interval. events and B for the amount of background events in a given interval.
This value assumes a gaussian error on the background so it will be This value assumes a gaussian error on the background so it will be
calculated for the 2 TeV masspoint where enough background events exist calculated for the 2 TeV masspoint of the decay to qW where enough
to justify this assumption. It follows from the central limit theorem background events exist to justify this assumption, which follows from
that states, that for identical distributed random variables, their sum the central limit theorem \autocite{CLT} that states, that for identical
converges to a gaussian distribution. The significance therefore distributed random variables, their sum converges to a gaussian
represents how good the signal can be distinguished from the background distribution. The significance represents how good the signal can be
in units of the standard deviation of the background. As interval, a 10 distinguished from the background in units of the standard deviation of
\% margin around the resonance nominal mass is chosen. The significance the background. As interval, a 10 \% margin around the resonance nominal
is then calculated for different selections on the discriminant of the mass is chosen. The significance is then calculated for different
two taggers and then plotted in dependence on the minimum resp. maximum selections on the discriminant of the two taggers and then plotted in
allowed value of the discriminant to pass the selection for the deep dependence on the minimum resp. maximum allowed value of the
boosted resp. the N-subjettiness tagger. discriminant to pass the selection for the DeepAK8 resp. the
N-subjettiness tagger.
The optimization process is done using only the data from year 2018, The optimisation process is done using only the data from year 2018,
assuming the taggers have similar performances on the data of the assuming the taggers have similar performances on the data of the
different years. different years.
@ -1266,36 +1269,35 @@ different years.
\begin{minipage}{0.5\textwidth} \begin{minipage}{0.5\textwidth}
\includegraphics{./figures/sig-tau.pdf} \includegraphics{./figures/sig-tau.pdf}
\end{minipage} \end{minipage}
\caption{Significance plots for the deep boosted (left) and N-subjettiness (right) tagger at the 2 TeV masspoint.} \caption{Significance plots for the DeepAK8 (left) and N-subjettiness (right) tagger at the 2 TeV masspoint.}
\label{fig:sig} \label{fig:sig}
\end{figure} \end{figure}
As a result, the \(\tau_{21}\) cut is placed at \(\le 0.35\), confirming As a result, the \(\tau_{21}\) cut is placed at \(\le 0.35\), confirming
the value previous research chose and the deep boosted cut is placed at the value previous research chose and the deep boosted cut is placed at
\(\ge 0.95\). For the deep boosted tagger, 0.97 would give a slightly \(\ge 0.95\). For the DeepAK8 tagger, 0.97 would give a slightly higher
higher significance but as it is very close to the edge where the significance but as it is very close to the edge where the significance
significance drops very low and the higher the cut the less background drops very low and the higher the cut the less background will be left
will be left to calculate the cross section limits, especially at higher to calculate the cross section limits, especially at higher resonance
resonance masses, the slightly less strict cut is chosen. masses, the slightly less strict cut is chosen.
For both taggers also a low purity category is introduced for high TeV For both taggers also a low purity category is introduced. Using the
regions. Using the cuts optimized for 2 TeV, there are very few cuts optimized for 2 TeV, there are very few background events left for
background events left for higher resonance masses, but to reliably higher resonance masses, but to reliably calculate cross section limits,
calculate cross section limits, those are needed. Therefore in the final those are needed. Therefore in the final cross section calculation, the
cross section calculation, the two categories are combined to have a two categories are combined to have a high signal sensitivity for all
high signal sensitivity for all masspoints between 1.6 TeV and 7 TeV masspoints between 1.6 TeV and 7 TeV that were simulated. As low purity
that were simulated. As low purity category for the N-subjettiness category for the N-subjettiness tagger, a cut at
tagger, a cut at \(0.35 < \tau_{21} < 0.75\) is used. For the deep \(0.35 < \tau_{21} < 0.75\) is used. For the DeepAK8 tagger the opposite
boosted tagger the opposite cut from the high purity category is used: cut from the high purity category is used: \(VvsQCD < 0.95\).
\(VvsQCD < 0.95\).
\newpage \newpage
\hypertarget{sec:extr}{% \hypertarget{sec:extr}{%
\section{Signal extraction}\label{sec:extr}} \section{Signal extraction}\label{sec:extr}}
After the optimization, now the optimal selection for the N-subjettiness After the optimisation, now the optimal selection for the N-subjettiness
as well as the deep boosted tagger is found and applied to the simulated as well as the DeepAK8 tagger is found and applied to the simulated
samples as well as the data collected by the CMS experiment. The fit samples as well as the data collected by the CMS experiment. The fit
described in Sec.~\ref{sec:moa} is performed for all masspoints of the described in Sec.~\ref{sec:moa} is performed for all masspoints of the
decay to qW and qZ and for the partial dataset of decay to qW and qZ and for the partial dataset of
@ -1350,8 +1352,8 @@ considered, as it might change the background shape a little and
therefore influence how many signal and background events are therefore influence how many signal and background events are
reconstructed from the data. reconstructed from the data.
Fourth, the uncertainty on the luminosity influence the normalization of Fourth, the uncertainty on the luminosity influences the normalization
the processes. Its value is 2.5 \% {[}cite todo{]}. \newpage of the processes. Its value is 2.5 \% \autocite{LUMI_UNC}. \newpage
\hypertarget{results}{% \hypertarget{results}{%
\section{Results}\label{results}} \section{Results}\label{results}}
@ -1375,18 +1377,17 @@ As described in Sec.~\ref{sec:extr}, the calculated cross section limits
are used to then calculate a mass limit, meaning the lowest possible are used to then calculate a mass limit, meaning the lowest possible
mass of the q* particle, by finding the crossing of the theory line with mass of the q* particle, by finding the crossing of the theory line with
the observed cross section limit. In Fig.~\ref{fig:res2016dw} it can be the observed cross section limit. In Fig.~\ref{fig:res2016dw} it can be
seen, that the observed limit using the deep boosted tagger in the seen, that the observed limit using the DeepAK8 tagger in the region
region where theory and observed limit cross is very high compared to where theory and observed limit cross is very high compared to when
when using the N-subjettiness tagger. Therefore the two lines cross at using the N-subjettiness tagger. Therefore the two lines cross at lower
lower resonance masses, which results in lower exclusion limits on the resonance masses, which results in lower exclusion limits on the mass of
mass of the q* particle causing the deep boosted tagger to perform worse the q* particle causing the DeepAK8 tagger to perform worse than the
than the N-subjettiness tagger in regards of establishing those limits N-subjettiness tagger in regards of establishing those limits as can be
as can be seen in Table~\ref{tbl:res2016}. The table also shows the seen in Table~\ref{tbl:res2016}. The table also shows the upper and
upper and lower limits on the mass found by calculating the crossing of lower limits on the mass found by calculating the crossing of the theory
the theory plus resp. minus its uncertainty. Due to the theory and the plus resp. minus its uncertainty. Due to the theory and the observed
observed limits line being slowly falling in the high TeV region, even a limits line being slowly falling in the high TeV region, even a small
small uncertainty of the theory can cause a high difference of the mass uncertainty of the theory can cause a high difference of the mass limit.
limit.
\hypertarget{tbl:res2016}{} \hypertarget{tbl:res2016}{}
\begin{longtable}[]{@{}lllll@{}} \begin{longtable}[]{@{}lllll@{}}
@ -1403,9 +1404,9 @@ Decay & Tagger & Limit {[}TeV{]} & Upper Limit {[}TeV{]} & Lower Limit
\midrule \midrule
\endhead \endhead
qW & \(\tau_{21}\) & 5.39 & 6.01 & 4.99\tabularnewline qW & \(\tau_{21}\) & 5.39 & 6.01 & 4.99\tabularnewline
qW & deep boosted & 4.96 & 5.19 & 4.84\tabularnewline qW & DeepAK8 & 4.96 & 5.19 & 4.84\tabularnewline
qZ & \(\tau_{21}\) & 4.86 & 4.96 & 4.70\tabularnewline qZ & \(\tau_{21}\) & 4.86 & 4.96 & 4.70\tabularnewline
qZ & deep boosted & 4.62 & 4.71 & 4.49\tabularnewline qZ & DeepAK8 & 4.62 & 4.71 & 4.49\tabularnewline
\bottomrule \bottomrule
\end{longtable} \end{longtable}
@ -1414,13 +1415,13 @@ qZ & deep boosted & 4.62 & 4.71 & 4.49\tabularnewline
\subfloat[Decay to qW, using N-subjettiness tagger]{% \subfloat[Decay to qW, using N-subjettiness tagger]{%
\label{fig:res2016tw}% \label{fig:res2016tw}%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_2016tau_13TeV.pdf}} \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_2016tau_13TeV.pdf}}
\subfloat[Decay to qW, using deep boosted tagger]{% \subfloat[Decay to qW, using DeepAK8 tagger]{%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_2016db_13TeV.pdf}% \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_2016db_13TeV.pdf}%
\label{fig:res2016dw}}\\ \label{fig:res2016dw}}\\
\subfloat[Decay to qZ, using N-subjettiness tagger]{% \subfloat[Decay to qZ, using N-subjettiness tagger]{%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_2016tau_13TeV.pdf}% \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_2016tau_13TeV.pdf}%
\label{fig:res2016tz}}% \label{fig:res2016tz}}%
\subfloat[Decay to qZ, using deep boosted tagger]{% \subfloat[Decay to qZ, using DeepAK8 tagger]{%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_2016db_13TeV.pdf}% \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_2016db_13TeV.pdf}%
\label{fig:res2016dz}}% \label{fig:res2016dz}}%
\caption{Results of the cross section limits for the partial dataset of 2016 using the $\tau_{21}$ tagger and the deep \caption{Results of the cross section limits for the partial dataset of 2016 using the $\tau_{21}$ tagger and the deep
@ -1461,8 +1462,8 @@ in Fig.~\ref{fig:prev}.
\begin{minipage}{0.5\textwidth} \begin{minipage}{0.5\textwidth}
\includegraphics{./figures/results/prev_qZ.png} \includegraphics{./figures/results/prev_qZ.png}
\end{minipage} \end{minipage}
\caption{Previous results of the cross section limits for q\* decaying to qW (left) and q\* decaying to qZ (right). \caption{Previous results of the cross section limits for q\* decaying to qW (left) and q\* decaying to qZ (right)
Taken from \cite{PREV_RESEARCH}.} \cite{PREV_RESEARCH}.}
\label{fig:prev} \label{fig:prev}
\end{figure} \end{figure}
@ -1493,9 +1494,9 @@ Decay & Tagger & Limit {[}TeV{]} & Upper Limit {[}TeV{]} & Lower Limit
\midrule \midrule
\endhead \endhead
qW & \(\tau_{21}\) & 6.00 & 6.26 & 5.74\tabularnewline qW & \(\tau_{21}\) & 6.00 & 6.26 & 5.74\tabularnewline
qW & deep boosted & 6.11 & 6.31 & 5.39\tabularnewline qW & DeepAK8 & 6.11 & 6.31 & 5.39\tabularnewline
qZ & \(\tau_{21}\) & 5.49 & 5.76 & 5.29\tabularnewline qZ & \(\tau_{21}\) & 5.49 & 5.76 & 5.29\tabularnewline
qZ & deep boosted & 4.95 & 5.13 & 4.85\tabularnewline qZ & DeepAK8 & 4.95 & 5.13 & 4.85\tabularnewline
\bottomrule \bottomrule
\end{longtable} \end{longtable}
@ -1510,16 +1511,16 @@ the decay to qZ than what was concluded by the previous research
\subfloat[Decay to qW, using N-subjettiness tagger]{% \subfloat[Decay to qW, using N-subjettiness tagger]{%
\label{fig:resCombinedtw}% \label{fig:resCombinedtw}%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_Combinedtau_13TeV.pdf}} \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_Combinedtau_13TeV.pdf}}
\subfloat[Decay to qW, using deep boosted tagger]{% \subfloat[Decay to qW, using DeepAK8 tagger]{%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_Combineddb_13TeV.pdf}% \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqW_Combineddb_13TeV.pdf}%
\label{fig:resCombineddw}}\\ \label{fig:resCombineddw}}\\
\subfloat[Decay to qZ, using N-subjettiness tagger]{% \subfloat[Decay to qZ, using N-subjettiness tagger]{%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_Combinedtau_13TeV.pdf}% \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_Combinedtau_13TeV.pdf}%
\label{fig:resCombinedtz}}% \label{fig:resCombinedtz}}%
\subfloat[Decay to qZ, using deep boosted tagger]{% \subfloat[Decay to qZ, using DeepAK8 tagger]{%
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_Combineddb_13TeV.pdf}% \includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_Combineddb_13TeV.pdf}%
\label{fig:resCombineddz}}% \label{fig:resCombineddz}}%
\caption{Results of the cross section limits for the combined dataset using the $\tau_{21}$ tagger and the deep boosted \caption{Results of the cross section limits for the combined dataset using the $\tau_{21}$ tagger and the DeepAK8
tagger.} tagger.}
\label{fig:resCombined} \label{fig:resCombined}
\end{figure} \end{figure}
@ -1527,12 +1528,12 @@ tagger.}
\hypertarget{comparison-of-taggers}{% \hypertarget{comparison-of-taggers}{%
\subsection{Comparison of taggers}\label{comparison-of-taggers}} \subsection{Comparison of taggers}\label{comparison-of-taggers}}
The results presented in Table~\ref{tbl:res2016} show, that the deep The results presented in Table~\ref{tbl:res2016} show, that the DeepAK8
boosted tagger was not able to significantly improve the results tagger was not able to significantly improve the results compared to the
compared to the N-subjettiness tagger. For further comparison, in N-subjettiness tagger. For further comparison, in
Fig.~\ref{fig:limit_comp} the expected limits of the different taggers Fig.~\ref{fig:limit_comp} the expected limits of the different taggers
for the q* \(\rightarrow\) qW and the q* \(\rightarrow\) qZ decay are for the q* \(\rightarrow\) qW and the q* \(\rightarrow\) qZ decay are
shown. It can be seen, that the deep boosted is at best as good as the shown. It can be seen, that the DeepAK8 is at best as good as the
N-subjettiness tagger. This was not the expected result, as the deep N-subjettiness tagger. This was not the expected result, as the deep
neural network was already found to provide a higher significance in the neural network was already found to provide a higher significance in the
optimisation done in Sec.~\ref{sec:opt}. The higher significance should optimisation done in Sec.~\ref{sec:opt}. The higher significance should
@ -1540,16 +1541,15 @@ also result in lower cross section limits. To make sure, there is no
mistake in the setup, also the expected cross section limits using only mistake in the setup, also the expected cross section limits using only
the high purity category of the two taggers with 2018 data are compared the high purity category of the two taggers with 2018 data are compared
in Fig.~\ref{fig:comp_2018}. There, the cross section limits calculated in Fig.~\ref{fig:comp_2018}. There, the cross section limits calculated
using the deep boosted tagger are a bit lower than with the using the DeepAK8 tagger are a bit lower than with the N-subjettiness
N-subjettiness tagger, showing, that the method used for optimisation is tagger, showing, that the method used for optimisation is working but
working but the assumption of it also applying to the combined dataset the assumption of it also applying to the combined dataset did not hold.
did not hold.
This can be explained by some training issues identified lately. The This can be explained by some training issues identified lately. The
training of the DeepAK8 tagger was done for the data of year 2016. It training of the DeepAK8 tagger was done for the data of year 2016. It
therefore performs differently for the data of the other years. This therefore performs differently for the data of the other years. This
caused the DeepAK8 tagger to perform significantly worse than it could caused the DeepAK8 tagger to perform significantly worse than it could
have for several reasons. First, the optimization done for the data of have for several reasons. First, the optimisation done for the data of
year 2018 could therefore not be applied to the other datasets. Second, year 2018 could therefore not be applied to the other datasets. Second,
even for the data of 2016, a newer version of the background simulation even for the data of 2016, a newer version of the background simulation
was used, that, in combination with the samples used for the signal, was used, that, in combination with the samples used for the signal,
@ -1557,6 +1557,7 @@ turned out to be the worst case scenario for the used training.
Recently, the training was improved to better perform across all Recently, the training was improved to better perform across all
datasets, but those changes could not be incorporated into this thesis datasets, but those changes could not be incorporated into this thesis
due to it not being possible to do this in a reasonable timeframe. due to it not being possible to do this in a reasonable timeframe.
\newpage
\begin{figure} \begin{figure}
\begin{minipage}{0.5\textwidth} \begin{minipage}{0.5\textwidth}
@ -1573,10 +1574,9 @@ decay to qZ}
\begin{figure} \begin{figure}
\hypertarget{fig:comp_2018}{% \hypertarget{fig:comp_2018}{%
\centering \centering
\includegraphics[width=0.7\textwidth,height=\textheight]{./figures/limit_comp_2018.pdf} \includegraphics[width=0.55\textwidth,height=\textheight]{./figures/limit_comp_2018.pdf}
\caption{Comparision of deep boosted and N-subjettiness tagger in the \caption{Comparison of DeepAK8 and N-subjettiness tagger in the high
high purity category using the data from year purity category using the data from year 2018.}\label{fig:comp_2018}
2018.}\label{fig:comp_2018}
} }
\end{figure} \end{figure}
@ -1593,7 +1593,7 @@ the CMS experiment at a centre-of-mass energy of
\(\sqrt{s} = \SI{13}{\tera\eV}\) has been searched. Also a partial \(\sqrt{s} = \SI{13}{\tera\eV}\) has been searched. Also a partial
dataset of \(\SI{35.92}{\per\femto\barn}\) was analysed, to be able to dataset of \(\SI{35.92}{\per\femto\barn}\) was analysed, to be able to
compare the results to previous research. Monte Carlo simulations were compare the results to previous research. Monte Carlo simulations were
used to estimate the QCD background and signal. used to model the QCD multijet background and signal shapes.
A selection was introduced to reduce background events and enhance A selection was introduced to reduce background events and enhance
signal sensitivity. This selection required at least two jets, a signal sensitivity. This selection required at least two jets, a
@ -1611,7 +1611,7 @@ the high TeV region. For the DeepAK8 tagger, a high purity category of
\(VvsQCD > 0.95\) and a low purity category of \(VvsQCD \le 0.95\) was \(VvsQCD > 0.95\) and a low purity category of \(VvsQCD \le 0.95\) was
used. For the N-subjettiness tagger the high purity category was used. For the N-subjettiness tagger the high purity category was
\(\tau_{21} < 0.35\) and the low purity category \(\tau_{21} < 0.35\) and the low purity category
\(0.35 < \tau_{21} < 0.75\). These values were obtained by optimizing \(0.35 < \tau_{21} < 0.75\). These values were obtained by optimising
for the highest possible significance of the signal. for the highest possible significance of the signal.
A combined fit to the dijet invariant mass distribution of background A combined fit to the dijet invariant mass distribution of background
@ -1624,10 +1624,14 @@ respectively 5.5 TeV for the decay to qZ. Those limits are about 1 TeV
higher than the ones found in previous research, that found them to be 5 higher than the ones found in previous research, that found them to be 5
TeV resp. 4.7 TeV. TeV resp. 4.7 TeV.
The DeepAK8 tagger performed worse than was expected. This can be The performance of the two taggers used have been compared and found to
explained with some training issues identified lately. Therefore, with produce similar results. This was unexpected, as the DeepAK8 tagger was
an updated training, it is expected that the presented results can be supposed to perform better than the N-subjettiness tagger. The result
further improved. analysing the decay to qW was \(\SI{0.1}{\tera\eV}\) better using the
DeepAK8 than with the N-subjettiness tagger, but analysing the decay to
qZ it was \(\SI{0.5}{\tera\eV}\) worse. The performance of the DeepAK8
tagger is likely to significantly improve with an updated training that
was not yet available in the framework used by this thesis.
\newpage \newpage
@ -1635,6 +1639,7 @@ further improved.
\printbibliography \printbibliography
\newpage
\appendix \appendix
\hypertarget{expected-and-observed-cross-section-limits}{% \hypertarget{expected-and-observed-cross-section-limits}{%
@ -1654,17 +1659,18 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.10406 & 0.14720 & 0.07371 & 0.08165\tabularnewline 1.6 & 0.10 & 0.15 & 0.074 & 0.082\tabularnewline
1.8 & 0.07656 & 0.10800 & 0.05441 & 0.04114\tabularnewline 1.8 & 0.077 & 0.11 & 0.054 & 0.041\tabularnewline
2.0 & 0.05422 & 0.07605 & 0.03879 & 0.04043\tabularnewline 2.0 & 0.054 & 0.076 & 0.039 & 0.040\tabularnewline
2.5 & 0.02430 & 0.03408 & 0.01747 & 0.04052\tabularnewline 2.5 & 0.024 & 0.034 & 0.017 & 0.041\tabularnewline
3.0 & 0.01262 & 0.01775 & 0.00904 & 0.02109\tabularnewline 3.0 & 0.013 & 0.018 & 0.009 & 0.021\tabularnewline
3.5 & 0.00703 & 0.00992 & 0.00502 & 0.00399\tabularnewline 3.5 & 0.0070 & 0.0099 & 0.005 & 0.004\tabularnewline
4.0 & 0.00424 & 0.00603 & 0.00300 & 0.00172\tabularnewline 4.0 & 0.0042 & 0.0060 & 0.003 & 0.0017\tabularnewline
4.5 & 0.00355 & 0.00478 & 0.00273 & 0.00249\tabularnewline 4.0 & 0.0042 & 0.0060 & 0.003 & 0.0017\tabularnewline
5.0 & 0.00269 & 0.00357 & 0.00211 & 0.00240\tabularnewline 4.5 & 0.0035 & 0.0048 & 0.0027 & 0.0025\tabularnewline
6.0 & 0.00103 & 0.00160 & 0.00068 & 0.00062\tabularnewline 5.0 & 0.0027 & 0.0036 & 0.0021 & 0.0024\tabularnewline
7.0 & 0.00063 & 0.00105 & 0.00039 & 0.00086\tabularnewline 6.0 & 0.0010 & 0.0016 & 0.00068 & 0.00062\tabularnewline
7.0 & 0.00063 & 0.0010 & 0.00039 & 0.00086\tabularnewline
\bottomrule \bottomrule
\end{longtable} \end{longtable}
@ -1681,17 +1687,17 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.17750 & 0.25179 & 0.12572 & 0.38242\tabularnewline 1.6 & 0.18 & 0.25 & 0.13 & 0.38\tabularnewline
1.8 & 0.11125 & 0.15870 & 0.07826 & 0.11692\tabularnewline 1.8 & 0.11 & 0.16 & 0.078 & 0.12\tabularnewline
2.0 & 0.08188 & 0.11549 & 0.05799 & 0.09528\tabularnewline 2.0 & 0.082 & 0.12 & 0.058 & 0.095\tabularnewline
2.5 & 0.03328 & 0.04668 & 0.02373 & 0.03653\tabularnewline 2.5 & 0.033 & 0.047 & 0.024 & 0.037\tabularnewline
3.0 & 0.01648 & 0.02338 & 0.01181 & 0.01108\tabularnewline 3.0 & 0.016 & 0.023 & 0.012 & 0.011\tabularnewline
3.5 & 0.00840 & 0.01195 & 0.00593 & 0.00683\tabularnewline 3.5 & 0.0084 & 0.012 & 0.0059 & 0.0068\tabularnewline
4.0 & 0.00459 & 0.00666 & 0.00322 & 0.00342\tabularnewline 4.0 & 0.0046 & 0.0067 & 0.0032 & 0.0034\tabularnewline
4.5 & 0.00276 & 0.00412 & 0.00190 & 0.00366\tabularnewline 4.5 & 0.0028 & 0.0041 & 0.0019 & 0.0037\tabularnewline
5.0 & 0.00177 & 0.00271 & 0.00118 & 0.00401\tabularnewline 5.0 & 0.0018 & 0.0027 & 0.0012 & 0.0040\tabularnewline
6.0 & 0.00110 & 0.00175 & 0.00071 & 0.00155\tabularnewline 6.0 & 0.0011 & 0.0017 & 0.00071 & 0.0016\tabularnewline
7.0 & 0.00065 & 0.00108 & 0.00041 & 0.00108\tabularnewline 7.0 & 0.00065 & 0.0011 & 0.00041 & 0.0011\tabularnewline
\bottomrule \bottomrule
\end{longtable} \end{longtable}
@ -1708,16 +1714,16 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.08687 & 0.12254 & 0.06174 & 0.06987\tabularnewline 1.6 & 0.087 & 0.12 & 0.062 & 0.07\tabularnewline
1.8 & 0.06719 & 0.09477 & 0.04832 & 0.03424\tabularnewline 1.8 & 0.067 & 0.095 & 0.048 & 0.034\tabularnewline
2.0 & 0.04734 & 0.06640 & 0.03405 & 0.03310\tabularnewline 2.0 & 0.047 & 0.066 & 0.034 & 0.033\tabularnewline
2.5 & 0.01867 & 0.02619 & 0.01343 & 0.03214\tabularnewline 2.5 & 0.019 & 0.026 & 0.013 & 0.032\tabularnewline
3.0 & 0.01043 & 0.01463 & 0.00744 & 0.01773\tabularnewline 3.0 & 0.010 & 0.015 & 0.0074 & 0.018\tabularnewline
3.5 & 0.00596 & 0.00840 & 0.00426 & 0.00347\tabularnewline 3.5 & 0.0060 & 0.0084 & 0.0043 & 0.0035\tabularnewline
4.0 & 0.00353 & 0.00500 & 0.00250 & 0.00140\tabularnewline 4.0 & 0.0035 & 0.0050 & 0.0025 & 0.0014\tabularnewline
4.5 & 0.00233 & 0.00335 & 0.00164 & 0.00181\tabularnewline 4.5 & 0.0023 & 0.0034 & 0.0016 & 0.0018\tabularnewline
5.0 & 0.00157 & 0.00231 & 0.00110 & 0.00188\tabularnewline 5.0 & 0.0016 & 0.0023 & 0.0011 & 0.0019\tabularnewline
6.0 & 0.00082 & 0.00126 & 0.00054 & 0.00049\tabularnewline 6.0 & 0.00082 & 0.0013 & 0.00054 & 0.00049\tabularnewline
7.0 & 0.00050 & 0.00083 & 0.00031 & 0.00066\tabularnewline 7.0 & 0.00050 & 0.00083 & 0.00031 & 0.00066\tabularnewline
\bottomrule \bottomrule
\end{longtable} \end{longtable}
@ -1735,17 +1741,17 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.16687 & 0.23805 & 0.11699 & 0.35999\tabularnewline 1.6 & 0.15 & 0.22 & 0.11 & 0.33\tabularnewline
1.8 & 0.12750 & 0.17934 & 0.09138 & 0.12891\tabularnewline 1.8 & 0.10 & 0.14 & 0.072 & 0.085\tabularnewline
2.0 & 0.09062 & 0.12783 & 0.06474 & 0.09977\tabularnewline 2.0 & 0.077 & 0.11 & 0.056 & 0.064\tabularnewline
2.5 & 0.03391 & 0.04783 & 0.02422 & 0.03754\tabularnewline 2.5 & 0.027 & 0.038 & 0.019 & 0.041\tabularnewline
3.0 & 0.01781 & 0.02513 & 0.01277 & 0.01159\tabularnewline 3.0 & 0.015 & 0.021 & 0.010 & 0.0087\tabularnewline
3.5 & 0.00949 & 0.01346 & 0.00678 & 0.00741\tabularnewline 3.5 & 0.0084 & 0.012 & 0.006 & 0.0066\tabularnewline
4.0 & 0.00494 & 0.00711 & 0.00349 & 0.00362\tabularnewline 4.0 & 0.0049 & 0.0071 & 0.0035 & 0.0045\tabularnewline
4.5 & 0.00293 & 0.00429 & 0.00203 & 0.00368\tabularnewline 4.5 & 0.0032 & 0.0046 & 0.0022 & 0.0026\tabularnewline
5.0 & 0.00188 & 0.00284 & 0.00127 & 0.00426\tabularnewline 5.0 & 0.0022 & 0.0033 & 0.0015 & 0.0041\tabularnewline
6.0 & 0.00102 & 0.00161 & 0.00066 & 0.00155\tabularnewline 6.0 & 0.0012 & 0.0019 & 0.00081 & 0.0018\tabularnewline
7.0 & 0.00053 & 0.00085 & 0.00034 & 0.00085\tabularnewline 7.0 & 0.00057 & 0.00092 & 0.00037 & 0.00093\tabularnewline
\bottomrule \bottomrule
\end{longtable} \end{longtable}
@ -1762,15 +1768,15 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.05703 & 0.07999 & 0.04088 & 0.03366\tabularnewline 1.6 & 0.057 & 0.08 & 0.041 & 0.034\tabularnewline
1.8 & 0.03953 & 0.05576 & 0.02833 & 0.04319\tabularnewline 1.8 & 0.040 & 0.056 & 0.028 & 0.043\tabularnewline
2.0 & 0.02844 & 0.03989 & 0.02045 & 0.04755\tabularnewline 2.0 & 0.028 & 0.040 & 0.020 & 0.048\tabularnewline
2.5 & 0.01270 & 0.01781 & 0.00913 & 0.01519\tabularnewline 2.5 & 0.013 & 0.018 & 0.0091 & 0.015\tabularnewline
3.0 & 0.00658 & 0.00923 & 0.00473 & 0.01218\tabularnewline 3.0 & 0.0066 & 0.0092 & 0.0047 & 0.012\tabularnewline
3.5 & 0.00376 & 0.00529 & 0.00269 & 0.00474\tabularnewline 3.5 & 0.0038 & 0.0053 & 0.0027 & 0.0047\tabularnewline
4.0 & 0.00218 & 0.00309 & 0.00156 & 0.00114\tabularnewline 4.0 & 0.0022 & 0.0031 & 0.0016 & 0.0011\tabularnewline
4.5 & 0.00132 & 0.00188 & 0.00094 & 0.00068\tabularnewline 4.5 & 0.0013 & 0.0019 & 0.00094 & 0.00068\tabularnewline
5.0 & 0.00084 & 0.00122 & 0.00060 & 0.00059\tabularnewline 5.0 & 0.00084 & 0.0012 & 0.00060 & 0.00059\tabularnewline
6.0 & 0.00044 & 0.00066 & 0.00030 & 0.00041\tabularnewline 6.0 & 0.00044 & 0.00066 & 0.00030 & 0.00041\tabularnewline
7.0 & 0.00022 & 0.00036 & 0.00014 & 0.00043\tabularnewline 7.0 & 0.00022 & 0.00036 & 0.00014 & 0.00043\tabularnewline
\bottomrule \bottomrule
@ -1789,15 +1795,15 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.06656 & 0.09495 & 0.04698 & 0.12374\tabularnewline 1.6 & 0.067 & 0.095 & 0.047 & 0.12\tabularnewline
1.8 & 0.04281 & 0.06141 & 0.03001 & 0.05422\tabularnewline 1.8 & 0.043 & 0.061 & 0.030 & 0.054\tabularnewline
2.0 & 0.03297 & 0.04650 & 0.02363 & 0.04658\tabularnewline 2.0 & 0.033 & 0.047 & 0.024 & 0.047\tabularnewline
2.5 & 0.01328 & 0.01868 & 0.00950 & 0.01109\tabularnewline 2.5 & 0.013 & 0.019 & 0.0095 & 0.011\tabularnewline
3.0 & 0.00650 & 0.00917 & 0.00464 & 0.00502\tabularnewline 3.0 & 0.0065 & 0.0092 & 0.0046 & 0.0050\tabularnewline
3.5 & 0.00338 & 0.00479 & 0.00241 & 0.00408\tabularnewline 3.5 & 0.0034 & 0.0048 & 0.0024 & 0.0041\tabularnewline
4.0 & 0.00182 & 0.00261 & 0.00129 & 0.00127\tabularnewline 4.0 & 0.0018 & 0.0026 & 0.0013 & 0.0013\tabularnewline
4.5 & 0.00107 & 0.00156 & 0.00074 & 0.00123\tabularnewline 4.5 & 0.0011 & 0.0016 & 0.00074 & 0.0012\tabularnewline
5.0 & 0.00068 & 0.00102 & 0.00046 & 0.00149\tabularnewline 5.0 & 0.00068 & 0.0010 & 0.00046 & 0.0015\tabularnewline
6.0 & 0.00038 & 0.00060 & 0.00024 & 0.00034\tabularnewline 6.0 & 0.00038 & 0.00060 & 0.00024 & 0.00034\tabularnewline
7.0 & 0.00021 & 0.00035 & 0.00013 & 0.00046\tabularnewline 7.0 & 0.00021 & 0.00035 & 0.00013 & 0.00046\tabularnewline
\bottomrule \bottomrule
@ -1816,15 +1822,15 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.05125 & 0.07188 & 0.03667 & 0.02993\tabularnewline 1.6 & 0.051 & 0.072 & 0.037 & 0.030\tabularnewline
1.8 & 0.03547 & 0.04989 & 0.02551 & 0.03614\tabularnewline 1.8 & 0.035 & 0.050 & 0.026 & 0.036\tabularnewline
2.0 & 0.02523 & 0.03539 & 0.01815 & 0.04177\tabularnewline 2.0 & 0.025 & 0.035 & 0.018 & 0.042\tabularnewline
2.5 & 0.01059 & 0.01485 & 0.00761 & 0.01230\tabularnewline 2.5 & 0.011 & 0.015 & 0.0076 & 0.012\tabularnewline
3.0 & 0.00576 & 0.00808 & 0.00412 & 0.01087\tabularnewline 3.0 & 0.0058 & 0.0081 & 0.0041 & 0.011\tabularnewline
3.5 & 0.00327 & 0.00460 & 0.00234 & 0.00425\tabularnewline 3.5 & 0.0033 & 0.0046 & 0.0023 & 0.0042\tabularnewline
4.0 & 0.00190 & 0.00269 & 0.00136 & 0.00097\tabularnewline 4.0 & 0.0019 & 0.0027 & 0.0014 & 0.00097\tabularnewline
4.5 & 0.00119 & 0.00168 & 0.00084 & 0.00059\tabularnewline 4.5 & 0.0012 & 0.0017 & 0.00084 & 0.00059\tabularnewline
5.0 & 0.00077 & 0.00110 & 0.00054 & 0.00051\tabularnewline 5.0 & 0.00077 & 0.0011 & 0.00054 & 0.00051\tabularnewline
6.0 & 0.00039 & 0.00057 & 0.00026 & 0.00036\tabularnewline 6.0 & 0.00039 & 0.00057 & 0.00026 & 0.00036\tabularnewline
7.0 & 0.00019 & 0.00031 & 0.00013 & 0.00036\tabularnewline 7.0 & 0.00019 & 0.00031 & 0.00013 & 0.00036\tabularnewline
\bottomrule \bottomrule
@ -1843,18 +1849,36 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
\midrule \midrule
\endhead \endhead
1.6 & 0.07719 & 0.10949 & 0.05467 & 0.14090\tabularnewline 1.6 & 0.067 & 0.095 & 0.047 & 0.095\tabularnewline
1.8 & 0.05297 & 0.07493 & 0.03752 & 0.06690\tabularnewline 1.8 & 0.044 & 0.063 & 0.032 & 0.048\tabularnewline
2.0 & 0.03875 & 0.05466 & 0.02768 & 0.05855\tabularnewline 2.0 & 0.032 & 0.045 & 0.023 & 0.045\tabularnewline
2.5 & 0.01512 & 0.02126 & 0.01080 & 0.01160\tabularnewline 2.5 & 0.012 & 0.017 & 0.0088 & 0.013\tabularnewline
3.0 & 0.00773 & 0.01088 & 0.00554 & 0.00548\tabularnewline 3.0 & 0.0064 & 0.009 & 0.0046 & 0.0032\tabularnewline
3.5 & 0.00400 & 0.00565 & 0.00285 & 0.00465\tabularnewline 3.5 & 0.0036 & 0.0051 & 0.0026 & 0.0039\tabularnewline
4.0 & 0.00211 & 0.00301 & 0.00149 & 0.00152\tabularnewline 4.0 & 0.0021 & 0.0029 & 0.0015 & 0.0027\tabularnewline
4.5 & 0.00118 & 0.00172 & 0.00082 & 0.00128\tabularnewline 4.5 & 0.0013 & 0.0018 & 0.00088 & 0.00094\tabularnewline
5.0 & 0.00073 & 0.00108 & 0.00050 & 0.00161\tabularnewline 5.0 & 0.00083 & 0.0012 & 0.00057 & 0.00150\tabularnewline
6.0 & 0.00039 & 0.00060 & 0.00025 & 0.00036\tabularnewline 6.0 & 0.00046 & 0.00072 & 0.00031 & 0.00043\tabularnewline
7.0 & 0.00021 & 0.00034 & 0.00013 & 0.00045\tabularnewline 7.0 & 0.00023 & 0.00037 & 0.00015 & 0.00049\tabularnewline
\bottomrule \bottomrule
\end{longtable} \end{longtable}
\newpage
\section*{Erklärung}
Hiermit bestätige ich, dass die vorliegende Bachelorarbeit von mir
selbstständig verfasst wurde und ich keine anderen als die angegebenen
Hilfsmittel - insbesondere keine im Quellenverzeichnis nicht benannten
Internet-Quellen - benutzt habe. Die Arbeit wurde bisher weder gesamt
noch in Teilen einer anderen Prüfungsbehörde vorgelegt. Die eingereichte
schriftliche Fassung entspricht der auf dem elekronischen
Speichermedium. Ich bin damit einverstanden, dass die Bachelorarbeit
veröffentlicht wird.
\vspace{5cm}
\parbox{5cm}{\hrule
\strut \footnotesize Ort, Datum} \hspace{1cm}\parbox{5cm}{\hrule
\strut \footnotesize David Leppla-Weber}
\end{document} \end{document}

20
thesis.toc
View File

@ -10,10 +10,10 @@
\contentsline {subsection}{\numberline {3.1}Large Hadron Collider}{6}{subsection.3.1}% \contentsline {subsection}{\numberline {3.1}Large Hadron Collider}{6}{subsection.3.1}%
\contentsline {subsection}{\numberline {3.2}Compact Muon Solenoid}{6}{subsection.3.2}% \contentsline {subsection}{\numberline {3.2}Compact Muon Solenoid}{6}{subsection.3.2}%
\contentsline {subsubsection}{\numberline {3.2.1}Coordinate conventions}{7}{subsubsection.3.2.1}% \contentsline {subsubsection}{\numberline {3.2.1}Coordinate conventions}{7}{subsubsection.3.2.1}%
\contentsline {subsubsection}{\numberline {3.2.2}The tracking system}{8}{subsubsection.3.2.2}% \contentsline {subsubsection}{\numberline {3.2.2}The tracking system}{7}{subsubsection.3.2.2}%
\contentsline {subsubsection}{\numberline {3.2.3}The electromagnetic calorimeter}{8}{subsubsection.3.2.3}% \contentsline {subsubsection}{\numberline {3.2.3}The electromagnetic calorimeter}{8}{subsubsection.3.2.3}%
\contentsline {subsubsection}{\numberline {3.2.4}The hadronic calorimeter}{8}{subsubsection.3.2.4}% \contentsline {subsubsection}{\numberline {3.2.4}The hadronic calorimeter}{8}{subsubsection.3.2.4}%
\contentsline {subsubsection}{\numberline {3.2.5}The solenoid}{9}{subsubsection.3.2.5}% \contentsline {subsubsection}{\numberline {3.2.5}The solenoid}{8}{subsubsection.3.2.5}%
\contentsline {subsubsection}{\numberline {3.2.6}The muon system}{9}{subsubsection.3.2.6}% \contentsline {subsubsection}{\numberline {3.2.6}The muon system}{9}{subsubsection.3.2.6}%
\contentsline {subsubsection}{\numberline {3.2.7}The Trigger system}{9}{subsubsection.3.2.7}% \contentsline {subsubsection}{\numberline {3.2.7}The Trigger system}{9}{subsubsection.3.2.7}%
\contentsline {subsubsection}{\numberline {3.2.8}The Particle Flow algorithm}{9}{subsubsection.3.2.8}% \contentsline {subsubsection}{\numberline {3.2.8}The Particle Flow algorithm}{9}{subsubsection.3.2.8}%
@ -23,17 +23,17 @@
\contentsline {section}{\numberline {5}Preselection and data quality}{13}{section.5}% \contentsline {section}{\numberline {5}Preselection and data quality}{13}{section.5}%
\contentsline {subsection}{\numberline {5.1}Preselection}{13}{subsection.5.1}% \contentsline {subsection}{\numberline {5.1}Preselection}{13}{subsection.5.1}%
\contentsline {subsection}{\numberline {5.2}Data - Monte Carlo Comparison}{15}{subsection.5.2}% \contentsline {subsection}{\numberline {5.2}Data - Monte Carlo Comparison}{15}{subsection.5.2}%
\contentsline {subsubsection}{\numberline {5.2.1}Sideband}{15}{subsubsection.5.2.1}% \contentsline {subsubsection}{\numberline {5.2.1}Sideband region}{15}{subsubsection.5.2.1}%
\contentsline {section}{\numberline {6}Jet substructure selection}{20}{section.6}% \contentsline {section}{\numberline {6}Jet substructure selection}{20}{section.6}%
\contentsline {subsection}{\numberline {6.1}N-Subjettiness}{20}{subsection.6.1}% \contentsline {subsection}{\numberline {6.1}N-Subjettiness}{20}{subsection.6.1}%
\contentsline {subsection}{\numberline {6.2}DeepAK8}{21}{subsection.6.2}% \contentsline {subsection}{\numberline {6.2}DeepAK8}{21}{subsection.6.2}%
\contentsline {subsection}{\numberline {6.3}Optimization}{22}{subsection.6.3}% \contentsline {subsection}{\numberline {6.3}Optimisation}{22}{subsection.6.3}%
\contentsline {section}{\numberline {7}Signal extraction}{24}{section.7}% \contentsline {section}{\numberline {7}Signal extraction}{24}{section.7}%
\contentsline {subsection}{\numberline {7.1}Systematic Uncertainties}{24}{subsection.7.1}% \contentsline {subsection}{\numberline {7.1}Systematic Uncertainties}{24}{subsection.7.1}%
\contentsline {section}{\numberline {8}Results}{26}{section.8}% \contentsline {section}{\numberline {8}Results}{25}{section.8}%
\contentsline {subsection}{\numberline {8.1}Partial dataset}{26}{subsection.8.1}% \contentsline {subsection}{\numberline {8.1}Partial dataset}{25}{subsection.8.1}%
\contentsline {subsubsection}{\numberline {8.1.1}Comparison with existing results}{26}{subsubsection.8.1.1}% \contentsline {subsubsection}{\numberline {8.1.1}Comparison with existing results}{25}{subsubsection.8.1.1}%
\contentsline {subsection}{\numberline {8.2}Combined dataset}{28}{subsection.8.2}% \contentsline {subsection}{\numberline {8.2}Combined dataset}{27}{subsection.8.2}%
\contentsline {subsection}{\numberline {8.3}Comparison of taggers}{30}{subsection.8.3}% \contentsline {subsection}{\numberline {8.3}Comparison of taggers}{29}{subsection.8.3}%
\contentsline {section}{\numberline {9}Summary}{32}{section.9}% \contentsline {section}{\numberline {9}Summary}{31}{section.9}%
\contentsline {section}{\numberline {A}Expected and observed cross section limits}{34}{appendix.A}% \contentsline {section}{\numberline {A}Expected and observed cross section limits}{34}{appendix.A}%