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appendix.md
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appendix.md
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\newpage
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\appendix
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# Expected and observed cross section limits
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| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
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|------------|-----------------|------------------|------------------|-----------------|
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| 1.6 | 0.10406 | 0.14720 | 0.07371 | 0.08165 |
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| 1.8 | 0.07656 | 0.10800 | 0.05441 | 0.04114 |
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| 2.0 | 0.05422 | 0.07605 | 0.03879 | 0.04043 |
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| 2.5 | 0.02430 | 0.03408 | 0.01747 | 0.04052 |
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| 3.0 | 0.01262 | 0.01775 | 0.00904 | 0.02109 |
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| 3.5 | 0.00703 | 0.00992 | 0.00502 | 0.00399 |
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| 4.0 | 0.00424 | 0.00603 | 0.00300 | 0.00172 |
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| 4.5 | 0.00355 | 0.00478 | 0.00273 | 0.00249 |
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| 5.0 | 0.00269 | 0.00357 | 0.00211 | 0.00240 |
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| 6.0 | 0.00103 | 0.00160 | 0.00068 | 0.00062 |
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| 7.0 | 0.00063 | 0.00105 | 0.00039 | 0.00086 |
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| 1.6 | 0.10 | 0.15 | 0.074 | 0.082 |
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| 1.8 | 0.077 | 0.11 | 0.054 | 0.041 |
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| 2.0 | 0.054 | 0.076 | 0.039 | 0.040 |
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| 2.5 | 0.024 | 0.034 | 0.017 | 0.041 |
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| 3.0 | 0.013 | 0.018 | 0.009 | 0.021 |
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| 3.5 | 0.0070 | 0.0099 | 0.005 | 0.004 |
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| 4.0 | 0.0042 | 0.0060 | 0.003 | 0.0017 |
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| 4.0 | 0.0042 | 0.0060 | 0.003 | 0.0017 |
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| 4.5 | 0.0035 | 0.0048 | 0.0027 | 0.0025 |
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| 5.0 | 0.0027 | 0.0036 | 0.0021 | 0.0024 |
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| 6.0 | 0.0010 | 0.0016 | 0.00068 | 0.00062 |
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| 7.0 | 0.00063 | 0.0010 | 0.00039 | 0.00086 |
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: Cross Section limits using 2016 data and the deep boosted tagger for the decay to qW
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| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
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|------------|-----------------|------------------|------------------|-----------------|
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| 1.6 | 0.17750 | 0.25179 | 0.12572 | 0.38242 |
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| 1.8 | 0.11125 | 0.15870 | 0.07826 | 0.11692 |
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| 2.0 | 0.08188 | 0.11549 | 0.05799 | 0.09528 |
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| 2.5 | 0.03328 | 0.04668 | 0.02373 | 0.03653 |
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| 3.0 | 0.01648 | 0.02338 | 0.01181 | 0.01108 |
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| 3.5 | 0.00840 | 0.01195 | 0.00593 | 0.00683 |
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| 4.0 | 0.00459 | 0.00666 | 0.00322 | 0.00342 |
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| 4.5 | 0.00276 | 0.00412 | 0.00190 | 0.00366 |
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| 5.0 | 0.00177 | 0.00271 | 0.00118 | 0.00401 |
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| 6.0 | 0.00110 | 0.00175 | 0.00071 | 0.00155 |
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| 7.0 | 0.00065 | 0.00108 | 0.00041 | 0.00108 |
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| 1.6 | 0.18 | 0.25 | 0.13 | 0.38 |
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| 1.8 | 0.11 | 0.16 | 0.078 | 0.12 |
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| 2.0 | 0.082 | 0.12 | 0.058 | 0.095 |
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| 2.5 | 0.033 | 0.047 | 0.024 | 0.037 |
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| 3.0 | 0.016 | 0.023 | 0.012 | 0.011 |
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| 3.5 | 0.0084 | 0.012 | 0.0059 | 0.0068 |
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| 4.0 | 0.0046 | 0.0067 | 0.0032 | 0.0034 |
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| 4.5 | 0.0028 | 0.0041 | 0.0019 | 0.0037 |
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| 5.0 | 0.0018 | 0.0027 | 0.0012 | 0.0040 |
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| 6.0 | 0.0011 | 0.0017 | 0.00071 | 0.0016 |
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| 7.0 | 0.00065 | 0.0011 | 0.00041 | 0.0011 |
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: Cross Section limits using 2016 data and the N-subjettiness tagger for the decay to qZ
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| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
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|------------|-----------------|------------------|------------------|-----------------|
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| 1.6 | 0.08687 | 0.12254 | 0.06174 | 0.06987 |
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| 1.8 | 0.06719 | 0.09477 | 0.04832 | 0.03424 |
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| 2.0 | 0.04734 | 0.06640 | 0.03405 | 0.03310 |
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| 2.5 | 0.01867 | 0.02619 | 0.01343 | 0.03214 |
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| 3.0 | 0.01043 | 0.01463 | 0.00744 | 0.01773 |
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| 3.5 | 0.00596 | 0.00840 | 0.00426 | 0.00347 |
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| 4.0 | 0.00353 | 0.00500 | 0.00250 | 0.00140 |
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| 4.5 | 0.00233 | 0.00335 | 0.00164 | 0.00181 |
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| 5.0 | 0.00157 | 0.00231 | 0.00110 | 0.00188 |
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| 6.0 | 0.00082 | 0.00126 | 0.00054 | 0.00049 |
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| 1.6 | 0.087 | 0.12 | 0.062 | 0.07 |
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| 1.8 | 0.067 | 0.095 | 0.048 | 0.034 |
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| 2.0 | 0.047 | 0.066 | 0.034 | 0.033 |
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| 2.5 | 0.019 | 0.026 | 0.013 | 0.032 |
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| 3.0 | 0.010 | 0.015 | 0.0074 | 0.018 |
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| 3.5 | 0.0060 | 0.0084 | 0.0043 | 0.0035 |
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| 4.0 | 0.0035 | 0.0050 | 0.0025 | 0.0014 |
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| 4.5 | 0.0023 | 0.0034 | 0.0016 | 0.0018 |
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| 5.0 | 0.0016 | 0.0023 | 0.0011 | 0.0019 |
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| 6.0 | 0.00082 | 0.0013 | 0.00054 | 0.00049 |
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| 7.0 | 0.00050 | 0.00083 | 0.00031 | 0.00066 |
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: Cross Section limits using 2016 data and deep boosted tagger for the decay to qZ
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| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
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|------------|-----------------|------------------|------------------|-----------------|
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| 1.6 | 0.16687 | 0.23805 | 0.11699 | 0.35999 |
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| 1.8 | 0.12750 | 0.17934 | 0.09138 | 0.12891 |
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| 2.0 | 0.09062 | 0.12783 | 0.06474 | 0.09977 |
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| 2.5 | 0.03391 | 0.04783 | 0.02422 | 0.03754 |
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| 3.0 | 0.01781 | 0.02513 | 0.01277 | 0.01159 |
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| 3.5 | 0.00949 | 0.01346 | 0.00678 | 0.00741 |
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| 4.0 | 0.00494 | 0.00711 | 0.00349 | 0.00362 |
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| 4.5 | 0.00293 | 0.00429 | 0.00203 | 0.00368 |
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| 5.0 | 0.00188 | 0.00284 | 0.00127 | 0.00426 |
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| 6.0 | 0.00102 | 0.00161 | 0.00066 | 0.00155 |
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| 7.0 | 0.00053 | 0.00085 | 0.00034 | 0.00085 |
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| 1.6 | 0.15 | 0.22 | 0.11 | 0.33 |
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| 1.8 | 0.10 | 0.14 | 0.072 | 0.085 |
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| 2.0 | 0.077 | 0.11 | 0.056 | 0.064 |
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| 2.5 | 0.027 | 0.038 | 0.019 | 0.041 |
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| 3.0 | 0.015 | 0.021 | 0.010 | 0.0087 |
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| 3.5 | 0.0084 | 0.012 | 0.006 | 0.0066 |
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| 4.0 | 0.0049 | 0.0071 | 0.0035 | 0.0045 |
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| 4.5 | 0.0032 | 0.0046 | 0.0022 | 0.0026 |
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| 5.0 | 0.0022 | 0.0033 | 0.0015 | 0.0041 |
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| 6.0 | 0.0012 | 0.0019 | 0.00081 | 0.0018 |
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| 7.0 | 0.00057 | 0.00092 | 0.00037 | 0.00093 |
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: Cross Section limits using the combined data and the N-subjettiness tagger for the decay to qW
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| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
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|------------|-----------------|------------------|------------------|-----------------|
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| 1.6 | 0.05703 | 0.07999 | 0.04088 | 0.03366 |
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| 1.8 | 0.03953 | 0.05576 | 0.02833 | 0.04319 |
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| 2.0 | 0.02844 | 0.03989 | 0.02045 | 0.04755 |
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| 2.5 | 0.01270 | 0.01781 | 0.00913 | 0.01519 |
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| 3.0 | 0.00658 | 0.00923 | 0.00473 | 0.01218 |
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| 3.5 | 0.00376 | 0.00529 | 0.00269 | 0.00474 |
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| 4.0 | 0.00218 | 0.00309 | 0.00156 | 0.00114 |
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| 4.5 | 0.00132 | 0.00188 | 0.00094 | 0.00068 |
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| 5.0 | 0.00084 | 0.00122 | 0.00060 | 0.00059 |
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| 1.6 | 0.057 | 0.08 | 0.041 | 0.034 |
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| 1.8 | 0.040 | 0.056 | 0.028 | 0.043 |
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| 2.0 | 0.028 | 0.040 | 0.020 | 0.048 |
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| 2.5 | 0.013 | 0.018 | 0.0091 | 0.015 |
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| 3.0 | 0.0066 | 0.0092 | 0.0047 | 0.012 |
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| 3.5 | 0.0038 | 0.0053 | 0.0027 | 0.0047 |
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| 4.0 | 0.0022 | 0.0031 | 0.0016 | 0.0011 |
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| 4.5 | 0.0013 | 0.0019 | 0.00094 | 0.00068 |
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| 5.0 | 0.00084 | 0.0012 | 0.00060 | 0.00059 |
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| 6.0 | 0.00044 | 0.00066 | 0.00030 | 0.00041 |
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| 7.0 | 0.00022 | 0.00036 | 0.00014 | 0.00043 |
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| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
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|------------|-----------------|------------------|------------------|-----------------|
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| 1.6 | 0.06656 | 0.09495 | 0.04698 | 0.12374 |
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| 1.8 | 0.04281 | 0.06141 | 0.03001 | 0.05422 |
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| 2.0 | 0.03297 | 0.04650 | 0.02363 | 0.04658 |
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| 2.5 | 0.01328 | 0.01868 | 0.00950 | 0.01109 |
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| 3.0 | 0.00650 | 0.00917 | 0.00464 | 0.00502 |
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| 3.5 | 0.00338 | 0.00479 | 0.00241 | 0.00408 |
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| 4.0 | 0.00182 | 0.00261 | 0.00129 | 0.00127 |
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| 4.5 | 0.00107 | 0.00156 | 0.00074 | 0.00123 |
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| 5.0 | 0.00068 | 0.00102 | 0.00046 | 0.00149 |
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| 1.6 | 0.067 | 0.095 | 0.047 | 0.12 |
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| 1.8 | 0.043 | 0.061 | 0.030 | 0.054 |
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| 2.0 | 0.033 | 0.047 | 0.024 | 0.047 |
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| 2.5 | 0.013 | 0.019 | 0.0095 | 0.011 |
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| 3.0 | 0.0065 | 0.0092 | 0.0046 | 0.0050 |
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| 3.5 | 0.0034 | 0.0048 | 0.0024 | 0.0041 |
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| 4.0 | 0.0018 | 0.0026 | 0.0013 | 0.0013 |
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| 4.5 | 0.0011 | 0.0016 | 0.00074 | 0.0012 |
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| 5.0 | 0.00068 | 0.0010 | 0.00046 | 0.0015 |
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| 6.0 | 0.00038 | 0.00060 | 0.00024 | 0.00034 |
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| 7.0 | 0.00021 | 0.00035 | 0.00013 | 0.00046 |
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: Cross Section limits using the combined data and the N-subjettiness tagger for the decay to qZ
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| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
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|------------|-----------------|------------------|------------------|-----------------|
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| 1.6 | 0.05125 | 0.07188 | 0.03667 | 0.02993 |
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| 1.8 | 0.03547 | 0.04989 | 0.02551 | 0.03614 |
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| 2.0 | 0.02523 | 0.03539 | 0.01815 | 0.04177 |
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| 2.5 | 0.01059 | 0.01485 | 0.00761 | 0.01230 |
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| 3.0 | 0.00576 | 0.00808 | 0.00412 | 0.01087 |
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| 3.5 | 0.00327 | 0.00460 | 0.00234 | 0.00425 |
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| 4.0 | 0.00190 | 0.00269 | 0.00136 | 0.00097 |
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| 4.5 | 0.00119 | 0.00168 | 0.00084 | 0.00059 |
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| 5.0 | 0.00077 | 0.00110 | 0.00054 | 0.00051 |
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| 1.6 | 0.051 | 0.072 | 0.037 | 0.030 |
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| 1.8 | 0.035 | 0.050 | 0.026 | 0.036 |
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| 2.0 | 0.025 | 0.035 | 0.018 | 0.042 |
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| 2.5 | 0.011 | 0.015 | 0.0076 | 0.012 |
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| 3.0 | 0.0058 | 0.0081 | 0.0041 | 0.011 |
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| 3.5 | 0.0033 | 0.0046 | 0.0023 | 0.0042 |
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| 4.0 | 0.0019 | 0.0027 | 0.0014 | 0.00097 |
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| 4.5 | 0.0012 | 0.0017 | 0.00084 | 0.00059 |
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| 5.0 | 0.00077 | 0.0011 | 0.00054 | 0.00051 |
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| 6.0 | 0.00039 | 0.00057 | 0.00026 | 0.00036 |
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| 7.0 | 0.00019 | 0.00031 | 0.00013 | 0.00036 |
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: Cross Section limits using the combined data and deep boosted tagger for the decay to qZ
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| Mass [TeV] | Exp. limit [pb] | Upper limit [pb] | Lower limit [pb] | Obs. limit [pb] |
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|------------|-----------------|------------------|------------------|-----------------|
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| 1.6 | 0.07719 | 0.10949 | 0.05467 | 0.14090 |
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| 1.8 | 0.05297 | 0.07493 | 0.03752 | 0.06690 |
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| 2.0 | 0.03875 | 0.05466 | 0.02768 | 0.05855 |
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| 2.5 | 0.01512 | 0.02126 | 0.01080 | 0.01160 |
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| 3.0 | 0.00773 | 0.01088 | 0.00554 | 0.00548 |
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| 3.5 | 0.00400 | 0.00565 | 0.00285 | 0.00465 |
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| 4.0 | 0.00211 | 0.00301 | 0.00149 | 0.00152 |
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| 4.5 | 0.00118 | 0.00172 | 0.00082 | 0.00128 |
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| 5.0 | 0.00073 | 0.00108 | 0.00050 | 0.00161 |
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| 6.0 | 0.00039 | 0.00060 | 0.00025 | 0.00036 |
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| 7.0 | 0.00021 | 0.00034 | 0.00013 | 0.00045 |
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| 1.6 | 0.067 | 0.095 | 0.047 | 0.095 |
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| 1.8 | 0.044 | 0.063 | 0.032 | 0.048 |
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| 2.0 | 0.032 | 0.045 | 0.023 | 0.045 |
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| 2.5 | 0.012 | 0.017 | 0.0088 | 0.013 |
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| 3.0 | 0.0064 | 0.009 | 0.0046 | 0.0032 |
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| 3.5 | 0.0036 | 0.0051 | 0.0026 | 0.0039 |
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| 4.0 | 0.0021 | 0.0029 | 0.0015 | 0.0027 |
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| 4.5 | 0.0013 | 0.0018 | 0.00088 | 0.00094 |
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| 5.0 | 0.00083 | 0.0012 | 0.00057 | 0.00150 |
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| 6.0 | 0.00046 | 0.00072 | 0.00031 | 0.00043 |
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| 7.0 | 0.00023 | 0.00037 | 0.00015 | 0.00049 |
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\newpage
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\section*{Erklärung}
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Hiermit bestätige ich, dass die vorliegende Bachelorarbeit von mir selbstständig verfasst wurde und ich keine anderen
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als die angegebenen Hilfsmittel - insbesondere keine im Quellenverzeichnis nicht benannten Internet-Quellen - benutzt
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habe. Die Arbeit wurde bisher weder gesamt noch in Teilen einer anderen Prüfungsbehörde vorgelegt. Die eingereichte
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schriftliche Fassung entspricht der auf dem elekronischen Speichermedium. Ich bin damit einverstanden, dass die
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Bachelorarbeit veröffentlicht wird.
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\vspace{5cm}
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\parbox{5cm}{\hrule
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\strut \footnotesize Ort, Datum} \hspace{1cm}\parbox{5cm}{\hrule
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\strut \footnotesize David Leppla-Weber}
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182
appendix.tex
182
appendix.tex
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\newpage
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\appendix
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\hypertarget{expected-and-observed-cross-section-limits}{%
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@ -17,17 +18,18 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
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limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
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\midrule
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\endhead
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1.6 & 0.10406 & 0.14720 & 0.07371 & 0.08165\tabularnewline
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1.8 & 0.07656 & 0.10800 & 0.05441 & 0.04114\tabularnewline
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2.0 & 0.05422 & 0.07605 & 0.03879 & 0.04043\tabularnewline
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2.5 & 0.02430 & 0.03408 & 0.01747 & 0.04052\tabularnewline
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3.0 & 0.01262 & 0.01775 & 0.00904 & 0.02109\tabularnewline
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3.5 & 0.00703 & 0.00992 & 0.00502 & 0.00399\tabularnewline
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4.0 & 0.00424 & 0.00603 & 0.00300 & 0.00172\tabularnewline
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4.5 & 0.00355 & 0.00478 & 0.00273 & 0.00249\tabularnewline
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5.0 & 0.00269 & 0.00357 & 0.00211 & 0.00240\tabularnewline
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6.0 & 0.00103 & 0.00160 & 0.00068 & 0.00062\tabularnewline
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7.0 & 0.00063 & 0.00105 & 0.00039 & 0.00086\tabularnewline
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1.6 & 0.10 & 0.15 & 0.074 & 0.082\tabularnewline
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1.8 & 0.077 & 0.11 & 0.054 & 0.041\tabularnewline
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2.0 & 0.054 & 0.076 & 0.039 & 0.040\tabularnewline
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2.5 & 0.024 & 0.034 & 0.017 & 0.041\tabularnewline
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3.0 & 0.013 & 0.018 & 0.009 & 0.021\tabularnewline
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3.5 & 0.0070 & 0.0099 & 0.005 & 0.004\tabularnewline
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4.0 & 0.0042 & 0.0060 & 0.003 & 0.0017\tabularnewline
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4.0 & 0.0042 & 0.0060 & 0.003 & 0.0017\tabularnewline
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4.5 & 0.0035 & 0.0048 & 0.0027 & 0.0025\tabularnewline
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5.0 & 0.0027 & 0.0036 & 0.0021 & 0.0024\tabularnewline
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6.0 & 0.0010 & 0.0016 & 0.00068 & 0.00062\tabularnewline
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7.0 & 0.00063 & 0.0010 & 0.00039 & 0.00086\tabularnewline
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\bottomrule
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\end{longtable}
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@ -44,17 +46,17 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
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limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
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\midrule
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\endhead
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1.6 & 0.17750 & 0.25179 & 0.12572 & 0.38242\tabularnewline
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1.8 & 0.11125 & 0.15870 & 0.07826 & 0.11692\tabularnewline
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2.0 & 0.08188 & 0.11549 & 0.05799 & 0.09528\tabularnewline
|
||||
2.5 & 0.03328 & 0.04668 & 0.02373 & 0.03653\tabularnewline
|
||||
3.0 & 0.01648 & 0.02338 & 0.01181 & 0.01108\tabularnewline
|
||||
3.5 & 0.00840 & 0.01195 & 0.00593 & 0.00683\tabularnewline
|
||||
4.0 & 0.00459 & 0.00666 & 0.00322 & 0.00342\tabularnewline
|
||||
4.5 & 0.00276 & 0.00412 & 0.00190 & 0.00366\tabularnewline
|
||||
5.0 & 0.00177 & 0.00271 & 0.00118 & 0.00401\tabularnewline
|
||||
6.0 & 0.00110 & 0.00175 & 0.00071 & 0.00155\tabularnewline
|
||||
7.0 & 0.00065 & 0.00108 & 0.00041 & 0.00108\tabularnewline
|
||||
1.6 & 0.18 & 0.25 & 0.13 & 0.38\tabularnewline
|
||||
1.8 & 0.11 & 0.16 & 0.078 & 0.12\tabularnewline
|
||||
2.0 & 0.082 & 0.12 & 0.058 & 0.095\tabularnewline
|
||||
2.5 & 0.033 & 0.047 & 0.024 & 0.037\tabularnewline
|
||||
3.0 & 0.016 & 0.023 & 0.012 & 0.011\tabularnewline
|
||||
3.5 & 0.0084 & 0.012 & 0.0059 & 0.0068\tabularnewline
|
||||
4.0 & 0.0046 & 0.0067 & 0.0032 & 0.0034\tabularnewline
|
||||
4.5 & 0.0028 & 0.0041 & 0.0019 & 0.0037\tabularnewline
|
||||
5.0 & 0.0018 & 0.0027 & 0.0012 & 0.0040\tabularnewline
|
||||
6.0 & 0.0011 & 0.0017 & 0.00071 & 0.0016\tabularnewline
|
||||
7.0 & 0.00065 & 0.0011 & 0.00041 & 0.0011\tabularnewline
|
||||
\bottomrule
|
||||
\end{longtable}
|
||||
|
||||
|
|
@ -71,16 +73,16 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
|
|||
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
|
||||
\midrule
|
||||
\endhead
|
||||
1.6 & 0.08687 & 0.12254 & 0.06174 & 0.06987\tabularnewline
|
||||
1.8 & 0.06719 & 0.09477 & 0.04832 & 0.03424\tabularnewline
|
||||
2.0 & 0.04734 & 0.06640 & 0.03405 & 0.03310\tabularnewline
|
||||
2.5 & 0.01867 & 0.02619 & 0.01343 & 0.03214\tabularnewline
|
||||
3.0 & 0.01043 & 0.01463 & 0.00744 & 0.01773\tabularnewline
|
||||
3.5 & 0.00596 & 0.00840 & 0.00426 & 0.00347\tabularnewline
|
||||
4.0 & 0.00353 & 0.00500 & 0.00250 & 0.00140\tabularnewline
|
||||
4.5 & 0.00233 & 0.00335 & 0.00164 & 0.00181\tabularnewline
|
||||
5.0 & 0.00157 & 0.00231 & 0.00110 & 0.00188\tabularnewline
|
||||
6.0 & 0.00082 & 0.00126 & 0.00054 & 0.00049\tabularnewline
|
||||
1.6 & 0.087 & 0.12 & 0.062 & 0.07\tabularnewline
|
||||
1.8 & 0.067 & 0.095 & 0.048 & 0.034\tabularnewline
|
||||
2.0 & 0.047 & 0.066 & 0.034 & 0.033\tabularnewline
|
||||
2.5 & 0.019 & 0.026 & 0.013 & 0.032\tabularnewline
|
||||
3.0 & 0.010 & 0.015 & 0.0074 & 0.018\tabularnewline
|
||||
3.5 & 0.0060 & 0.0084 & 0.0043 & 0.0035\tabularnewline
|
||||
4.0 & 0.0035 & 0.0050 & 0.0025 & 0.0014\tabularnewline
|
||||
4.5 & 0.0023 & 0.0034 & 0.0016 & 0.0018\tabularnewline
|
||||
5.0 & 0.0016 & 0.0023 & 0.0011 & 0.0019\tabularnewline
|
||||
6.0 & 0.00082 & 0.0013 & 0.00054 & 0.00049\tabularnewline
|
||||
7.0 & 0.00050 & 0.00083 & 0.00031 & 0.00066\tabularnewline
|
||||
\bottomrule
|
||||
\end{longtable}
|
||||
|
|
@ -98,17 +100,17 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
|
|||
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
|
||||
\midrule
|
||||
\endhead
|
||||
1.6 & 0.16687 & 0.23805 & 0.11699 & 0.35999\tabularnewline
|
||||
1.8 & 0.12750 & 0.17934 & 0.09138 & 0.12891\tabularnewline
|
||||
2.0 & 0.09062 & 0.12783 & 0.06474 & 0.09977\tabularnewline
|
||||
2.5 & 0.03391 & 0.04783 & 0.02422 & 0.03754\tabularnewline
|
||||
3.0 & 0.01781 & 0.02513 & 0.01277 & 0.01159\tabularnewline
|
||||
3.5 & 0.00949 & 0.01346 & 0.00678 & 0.00741\tabularnewline
|
||||
4.0 & 0.00494 & 0.00711 & 0.00349 & 0.00362\tabularnewline
|
||||
4.5 & 0.00293 & 0.00429 & 0.00203 & 0.00368\tabularnewline
|
||||
5.0 & 0.00188 & 0.00284 & 0.00127 & 0.00426\tabularnewline
|
||||
6.0 & 0.00102 & 0.00161 & 0.00066 & 0.00155\tabularnewline
|
||||
7.0 & 0.00053 & 0.00085 & 0.00034 & 0.00085\tabularnewline
|
||||
1.6 & 0.15 & 0.22 & 0.11 & 0.33\tabularnewline
|
||||
1.8 & 0.10 & 0.14 & 0.072 & 0.085\tabularnewline
|
||||
2.0 & 0.077 & 0.11 & 0.056 & 0.064\tabularnewline
|
||||
2.5 & 0.027 & 0.038 & 0.019 & 0.041\tabularnewline
|
||||
3.0 & 0.015 & 0.021 & 0.010 & 0.0087\tabularnewline
|
||||
3.5 & 0.0084 & 0.012 & 0.006 & 0.0066\tabularnewline
|
||||
4.0 & 0.0049 & 0.0071 & 0.0035 & 0.0045\tabularnewline
|
||||
4.5 & 0.0032 & 0.0046 & 0.0022 & 0.0026\tabularnewline
|
||||
5.0 & 0.0022 & 0.0033 & 0.0015 & 0.0041\tabularnewline
|
||||
6.0 & 0.0012 & 0.0019 & 0.00081 & 0.0018\tabularnewline
|
||||
7.0 & 0.00057 & 0.00092 & 0.00037 & 0.00093\tabularnewline
|
||||
\bottomrule
|
||||
\end{longtable}
|
||||
|
||||
|
|
@ -125,15 +127,15 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
|
|||
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
|
||||
\midrule
|
||||
\endhead
|
||||
1.6 & 0.05703 & 0.07999 & 0.04088 & 0.03366\tabularnewline
|
||||
1.8 & 0.03953 & 0.05576 & 0.02833 & 0.04319\tabularnewline
|
||||
2.0 & 0.02844 & 0.03989 & 0.02045 & 0.04755\tabularnewline
|
||||
2.5 & 0.01270 & 0.01781 & 0.00913 & 0.01519\tabularnewline
|
||||
3.0 & 0.00658 & 0.00923 & 0.00473 & 0.01218\tabularnewline
|
||||
3.5 & 0.00376 & 0.00529 & 0.00269 & 0.00474\tabularnewline
|
||||
4.0 & 0.00218 & 0.00309 & 0.00156 & 0.00114\tabularnewline
|
||||
4.5 & 0.00132 & 0.00188 & 0.00094 & 0.00068\tabularnewline
|
||||
5.0 & 0.00084 & 0.00122 & 0.00060 & 0.00059\tabularnewline
|
||||
1.6 & 0.057 & 0.08 & 0.041 & 0.034\tabularnewline
|
||||
1.8 & 0.040 & 0.056 & 0.028 & 0.043\tabularnewline
|
||||
2.0 & 0.028 & 0.040 & 0.020 & 0.048\tabularnewline
|
||||
2.5 & 0.013 & 0.018 & 0.0091 & 0.015\tabularnewline
|
||||
3.0 & 0.0066 & 0.0092 & 0.0047 & 0.012\tabularnewline
|
||||
3.5 & 0.0038 & 0.0053 & 0.0027 & 0.0047\tabularnewline
|
||||
4.0 & 0.0022 & 0.0031 & 0.0016 & 0.0011\tabularnewline
|
||||
4.5 & 0.0013 & 0.0019 & 0.00094 & 0.00068\tabularnewline
|
||||
5.0 & 0.00084 & 0.0012 & 0.00060 & 0.00059\tabularnewline
|
||||
6.0 & 0.00044 & 0.00066 & 0.00030 & 0.00041\tabularnewline
|
||||
7.0 & 0.00022 & 0.00036 & 0.00014 & 0.00043\tabularnewline
|
||||
\bottomrule
|
||||
|
|
@ -152,15 +154,15 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
|
|||
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
|
||||
\midrule
|
||||
\endhead
|
||||
1.6 & 0.06656 & 0.09495 & 0.04698 & 0.12374\tabularnewline
|
||||
1.8 & 0.04281 & 0.06141 & 0.03001 & 0.05422\tabularnewline
|
||||
2.0 & 0.03297 & 0.04650 & 0.02363 & 0.04658\tabularnewline
|
||||
2.5 & 0.01328 & 0.01868 & 0.00950 & 0.01109\tabularnewline
|
||||
3.0 & 0.00650 & 0.00917 & 0.00464 & 0.00502\tabularnewline
|
||||
3.5 & 0.00338 & 0.00479 & 0.00241 & 0.00408\tabularnewline
|
||||
4.0 & 0.00182 & 0.00261 & 0.00129 & 0.00127\tabularnewline
|
||||
4.5 & 0.00107 & 0.00156 & 0.00074 & 0.00123\tabularnewline
|
||||
5.0 & 0.00068 & 0.00102 & 0.00046 & 0.00149\tabularnewline
|
||||
1.6 & 0.067 & 0.095 & 0.047 & 0.12\tabularnewline
|
||||
1.8 & 0.043 & 0.061 & 0.030 & 0.054\tabularnewline
|
||||
2.0 & 0.033 & 0.047 & 0.024 & 0.047\tabularnewline
|
||||
2.5 & 0.013 & 0.019 & 0.0095 & 0.011\tabularnewline
|
||||
3.0 & 0.0065 & 0.0092 & 0.0046 & 0.0050\tabularnewline
|
||||
3.5 & 0.0034 & 0.0048 & 0.0024 & 0.0041\tabularnewline
|
||||
4.0 & 0.0018 & 0.0026 & 0.0013 & 0.0013\tabularnewline
|
||||
4.5 & 0.0011 & 0.0016 & 0.00074 & 0.0012\tabularnewline
|
||||
5.0 & 0.00068 & 0.0010 & 0.00046 & 0.0015\tabularnewline
|
||||
6.0 & 0.00038 & 0.00060 & 0.00024 & 0.00034\tabularnewline
|
||||
7.0 & 0.00021 & 0.00035 & 0.00013 & 0.00046\tabularnewline
|
||||
\bottomrule
|
||||
|
|
@ -179,15 +181,15 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
|
|||
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
|
||||
\midrule
|
||||
\endhead
|
||||
1.6 & 0.05125 & 0.07188 & 0.03667 & 0.02993\tabularnewline
|
||||
1.8 & 0.03547 & 0.04989 & 0.02551 & 0.03614\tabularnewline
|
||||
2.0 & 0.02523 & 0.03539 & 0.01815 & 0.04177\tabularnewline
|
||||
2.5 & 0.01059 & 0.01485 & 0.00761 & 0.01230\tabularnewline
|
||||
3.0 & 0.00576 & 0.00808 & 0.00412 & 0.01087\tabularnewline
|
||||
3.5 & 0.00327 & 0.00460 & 0.00234 & 0.00425\tabularnewline
|
||||
4.0 & 0.00190 & 0.00269 & 0.00136 & 0.00097\tabularnewline
|
||||
4.5 & 0.00119 & 0.00168 & 0.00084 & 0.00059\tabularnewline
|
||||
5.0 & 0.00077 & 0.00110 & 0.00054 & 0.00051\tabularnewline
|
||||
1.6 & 0.051 & 0.072 & 0.037 & 0.030\tabularnewline
|
||||
1.8 & 0.035 & 0.050 & 0.026 & 0.036\tabularnewline
|
||||
2.0 & 0.025 & 0.035 & 0.018 & 0.042\tabularnewline
|
||||
2.5 & 0.011 & 0.015 & 0.0076 & 0.012\tabularnewline
|
||||
3.0 & 0.0058 & 0.0081 & 0.0041 & 0.011\tabularnewline
|
||||
3.5 & 0.0033 & 0.0046 & 0.0023 & 0.0042\tabularnewline
|
||||
4.0 & 0.0019 & 0.0027 & 0.0014 & 0.00097\tabularnewline
|
||||
4.5 & 0.0012 & 0.0017 & 0.00084 & 0.00059\tabularnewline
|
||||
5.0 & 0.00077 & 0.0011 & 0.00054 & 0.00051\tabularnewline
|
||||
6.0 & 0.00039 & 0.00057 & 0.00026 & 0.00036\tabularnewline
|
||||
7.0 & 0.00019 & 0.00031 & 0.00013 & 0.00036\tabularnewline
|
||||
\bottomrule
|
||||
|
|
@ -206,16 +208,34 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
|
|||
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
|
||||
\midrule
|
||||
\endhead
|
||||
1.6 & 0.07719 & 0.10949 & 0.05467 & 0.14090\tabularnewline
|
||||
1.8 & 0.05297 & 0.07493 & 0.03752 & 0.06690\tabularnewline
|
||||
2.0 & 0.03875 & 0.05466 & 0.02768 & 0.05855\tabularnewline
|
||||
2.5 & 0.01512 & 0.02126 & 0.01080 & 0.01160\tabularnewline
|
||||
3.0 & 0.00773 & 0.01088 & 0.00554 & 0.00548\tabularnewline
|
||||
3.5 & 0.00400 & 0.00565 & 0.00285 & 0.00465\tabularnewline
|
||||
4.0 & 0.00211 & 0.00301 & 0.00149 & 0.00152\tabularnewline
|
||||
4.5 & 0.00118 & 0.00172 & 0.00082 & 0.00128\tabularnewline
|
||||
5.0 & 0.00073 & 0.00108 & 0.00050 & 0.00161\tabularnewline
|
||||
6.0 & 0.00039 & 0.00060 & 0.00025 & 0.00036\tabularnewline
|
||||
7.0 & 0.00021 & 0.00034 & 0.00013 & 0.00045\tabularnewline
|
||||
1.6 & 0.067 & 0.095 & 0.047 & 0.095\tabularnewline
|
||||
1.8 & 0.044 & 0.063 & 0.032 & 0.048\tabularnewline
|
||||
2.0 & 0.032 & 0.045 & 0.023 & 0.045\tabularnewline
|
||||
2.5 & 0.012 & 0.017 & 0.0088 & 0.013\tabularnewline
|
||||
3.0 & 0.0064 & 0.009 & 0.0046 & 0.0032\tabularnewline
|
||||
3.5 & 0.0036 & 0.0051 & 0.0026 & 0.0039\tabularnewline
|
||||
4.0 & 0.0021 & 0.0029 & 0.0015 & 0.0027\tabularnewline
|
||||
4.5 & 0.0013 & 0.0018 & 0.00088 & 0.00094\tabularnewline
|
||||
5.0 & 0.00083 & 0.0012 & 0.00057 & 0.00150\tabularnewline
|
||||
6.0 & 0.00046 & 0.00072 & 0.00031 & 0.00043\tabularnewline
|
||||
7.0 & 0.00023 & 0.00037 & 0.00015 & 0.00049\tabularnewline
|
||||
\bottomrule
|
||||
\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}
|
||||
|
|
|
|||
|
|
@ -1649,3 +1649,53 @@ archivePrefix = {arXiv},
|
|||
reportNumber = "CMS-NOTE-2005-001, CERN-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
make.sh
2
make.sh
|
|
@ -1,5 +1,5 @@
|
|||
#!/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
|
||||
lualatex thesis
|
||||
biber thesis
|
||||
|
|
|
|||
226
thesis.aux
226
thesis.aux
|
|
@ -37,15 +37,15 @@
|
|||
\newlabel{theoretical-motivation}{{2}{2}{Theoretical motivation}{section.2}{}}
|
||||
\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}Standard Model}{2}{subsection.2.1}\protected@file@percent }
|
||||
\newlabel{sec:sm}{{2.1}{2}{Standard Model}{subsection.2.1}{}}
|
||||
\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Elementary particles of the Standard Model and their mass charge and spin. Taken from \autocite {SM}\relax }}{2}{figure.caption.2}\protected@file@percent }
|
||||
\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Elementary particles of the Standard Model and their mass charge and spin \autocite {SM}.\relax }}{2}{figure.caption.2}\protected@file@percent }
|
||||
\providecommand*\caption@xref[2]{\@setref\relax\@undefined{#1}}
|
||||
\newlabel{fig:sm}{{1}{2}{Elementary particles of the Standard Model and their mass charge and spin. Taken from \autocite {SM}\relax }{figure.caption.2}{}}
|
||||
\newlabel{fig:sm}{{1}{2}{Elementary particles of the Standard Model and their mass charge and spin \autocite {SM}.\relax }{figure.caption.2}{}}
|
||||
\abx@aux@cite{HADRONIZATION}
|
||||
\abx@aux@segm{0}{0}{HADRONIZATION}
|
||||
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This is LuaTeX, Version 1.10.0 (TeX Live 2019/Arch Linux) (format=lualatex 2019.8.29) 31 OCT 2019 19:46
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thesis.md
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thesis.md
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|
@ -9,7 +9,7 @@ header-includes: |
|
|||
\usepackage{csquotes}
|
||||
\usepackage{abstract}
|
||||
\pagenumbering{gobble}
|
||||
\setlength{\parskip}{0.5em}
|
||||
\setlength{\parskip}{0.4em}
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\bibliographystyle{lucas_unsrt}
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documentclass: article
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geometry:
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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
|
||||
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
|
||||
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.
|
||||
|
||||
\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
|
||||
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
|
||||
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
|
||||
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.
|
||||
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
|
||||
electron, the muon and the tau. The three quark generations consist of first, the up and down, second, the charm and
|
||||
strange, and third, the top and bottom quark. A full list of particles of the standard model can be found in [@fig:sm].
|
||||
Furthermore, all fermions have an associated anti particle with reversed charge. Bound states of multiple quarks also
|
||||
exist and are called hadrons.
|
||||
electron, the muon and the tau. The three quark generations consist of the up and down, the charm and strange, and the
|
||||
top and bottom quark. A full list of particles of the standard model can be found in [@fig:sm]. Furthermore, all
|
||||
fermions have an associated anti particle with reversed charge. Bound states of multiple quarks also exist and are
|
||||
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}
|
||||
|
||||
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
|
||||
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
|
||||
$\SI{44.98}{\per\femto\barn}$ and in 2018 $\SI{63.67}{\per\femto\barn}$. Because of eventual inconsistencies in the
|
||||
setup, some data have to be discarded. The amount of usable data is $\SI{34.92}{\per\femto\barn}$,
|
||||
$\SI{41.53}{\per\femto\barn}$ and $\SI{59.74}{\per\femto\barn}$ for the years 2016, 2017 and 2018, totalling to
|
||||
$\SI{137.19}{\per\femto\barn}$ of data.
|
||||
$\SI{44.98}{\per\femto\barn}$ and in 2018 $\SI{63.67}{\per\femto\barn}$ [@CMS_LUMI]. The amount of data usable for
|
||||
research is $\SI{35.92}{\per\femto\barn}$, $\SI{41.53}{\per\femto\barn}$ and $\SI{59.74}{\per\femto\barn}$ for the years
|
||||
2016, 2017 and 2018, totalling to $\SI{137.19}{\per\femto\barn}$ of data.
|
||||
|
||||
![
|
||||
The setup of the Compact Muon Solenoid showing its onion like structure, the different detector parts and where
|
||||
|
|
@ -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.
|
||||
|
||||
![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%}
|
||||
|
||||
### 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
|
||||
with many random soft "ghosts". Taken from [@ANTIKT]
|
||||
with many random soft "ghosts" [@ANTIKT].
|
||||
](./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
|
||||
|
|
@ -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
|
||||
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
|
||||
MadGraph and Pythia 8. Later on, also detector effects (like its limited resolution) are applied to make sure, they look
|
||||
like real data coming from the CMS detector.
|
||||
MadGraph [@MADGRAPH] and Pythia 8 [@PYTHIA8]. Later on, also detector effects (like its limited resolution) are applied
|
||||
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
|
||||
section of $\SI{1}{\per\pico\barn}$. The simulation was done using MadGraph for eleven masspoints between 1.6 TeV and 7
|
||||
TeV. Because of the expected high mass, the signal width will be dominated by the resolution of the detector, not by the
|
||||
natural resonance width.
|
||||
section of $\SI{1}{\pico\barn}$. The simulation was done using MadGraph [@MADGRAPH] for eleven masspoints between 1.6
|
||||
TeV and 7 TeV. Because of the expected high mass, the signal width will be dominated by the resolution of the detector,
|
||||
not by the natural resonance width.
|
||||
|
||||
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:
|
||||
|
|
@ -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
|
||||
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
|
||||
simulated signal cross section of $\SI{1}{\per\pico\barn}$. The resulting coefficients of said combination then show the
|
||||
expected signal rate for the simulated cross section. An example of such a fit can be seen in [@fig:cb_fit]. In this
|
||||
figure, a binning of 200 GeV is used for presentational purposes. The analysis itself is conducted using a 1 GeV
|
||||
binning. It can be seen that the fit works very well and therefore confirms the functions chosen 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 $\pm$ 23
|
||||
$\si{\giga\eV}$ which is in very good agreement with the expected 3000 GeV mean. Those numbers clearly show that the
|
||||
method in use is able to successfully describe the simulated toy data.
|
||||
A linear combination of the signal and background function is then fitted to a toy dataset with gaussian errors obtained
|
||||
by adding simulated background and signal. The resulting coefficients of said combination then show the expected signal
|
||||
rate for the simulated signal cross section of $\SI{1}{\pico\barn}$. An example of such a fit can be seen in
|
||||
[@fig:cb_fit]. In this figure, a binning of 200 GeV is used for presentational purposes. The analysis itself is
|
||||
conducted using a 1 GeV binning. It can be seen that the fit works very well and therefore confirms the functions chosen
|
||||
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
|
||||
$\pm$ 23 $\si{\giga\eV}$ which is in very good agreement with the expected 3000 GeV mean. Those numbers clearly show
|
||||
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.
|
||||
|
|
@ -573,6 +572,7 @@ events.
|
|||
\includegraphics{./figures/2016/v1_Njet_N_jets_stack.eps}
|
||||
\end{minipage}
|
||||
\begin{minipage}{\textwidth}
|
||||
\vspace{0.1cm}
|
||||
\centering\textbf{Comparison for the combined dataset}
|
||||
\end{minipage}
|
||||
\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
|
||||
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
|
||||
as in previous research of $\Delta\eta \le 1.3$ is used. The comparison of the $m_{jj}$ distribution seen in [@fig:deta]
|
||||
before and after the cut clearly shows, that the signal sensitivity was greatly improved by this cut.
|
||||
as in previous research of $\Delta\eta \le 1.3$ is used. In the top two distributions of [@fig:deta], this cut is marked
|
||||
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{minipage}{\textwidth}
|
||||
|
|
@ -625,7 +626,8 @@ before and after the cut clearly shows, that the signal sensitivity was greatly
|
|||
\begin{minipage}{0.5\textwidth}
|
||||
\includegraphics{./figures/combined/v1_Eta_invMass_stack.eps}
|
||||
\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 $\SI{137.19}{\per\femto\barn}$.
|
||||
}
|
||||
\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
|
||||
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{minipage}{0.5\textwidth}
|
||||
|
|
@ -718,7 +720,7 @@ were published by the CMS group. [cite todo]
|
|||
\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
|
||||
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
|
||||
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.
|
||||
|
||||
## 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
|
||||
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
|
||||
the one used for this research) and $\tau_{21}$ tagger used in this analysis.
|
||||
Taken from [@DEEP_BOOSTED]](./figures/deep_ak8.pdf){#fig:ak8_eff width=60%}
|
||||
![Comparison of tagger efficiencies, showing, between others, the DeepAK8 and $\tau_{21}$ tagger used in this analysis
|
||||
[@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
|
||||
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
|
||||
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,
|
||||
that is trained to quantify how strongly the output of the non-decorrelated tagger is correlated to the mass of a
|
||||
particle. Its output is fed back to the network as a penalty so it avoids using features of the particles correlated to
|
||||
their mass. The result is a largely mass decorrelated tagger of heavy resonances, that doesn't introduce a bias in the
|
||||
jet mass shape. As can be seen in [@fig:ak8_eff], it performs not as good as the non-mass-decorrelated version, but
|
||||
still better than the other taggers it was compared to.
|
||||
In this thesis, the mass decorrelated version of the DeepAK8 tagger, called DeepAK8-MD but further referred to as only
|
||||
DeepAK8, is used. It adds an additional mass predictor layer, that is trained to quantify how strongly the output of the
|
||||
non-decorrelated tagger is correlated to the mass of a particle. Its output is fed back to the network as a penalty so
|
||||
it avoids using features of the particles correlated to their mass. The result is a largely mass decorrelated tagger of
|
||||
heavy resonances, that doesn't introduce a bias in the jet mass shape. As can be seen in [@fig:ak8_eff], it performs not
|
||||
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
|
||||
be caused by the decay of a vector boson. Therefore, using the same way to choose a candidate jet as for the
|
||||
N-subjettiness tagger, a selection is applied so that this candidate jet has a WvsQCD/ZvsQCD value greater than some
|
||||
value determined by the optimization presented next.
|
||||
The higher the discriminant value, called WvsQCD resp. ZvsQCD (further referred to as only VvsQCD), of the DeepAK8
|
||||
tagger, the more likely is the jet to be caused by the decay of a vector boson. Therefore, using the same way to choose
|
||||
a candidate jet as for the N-subjettiness tagger, a selection is applied so that this candidate jet has a VvsQCD value
|
||||
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
|
||||
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
|
||||
error on the background so it will be calculated for the 2 TeV masspoint where enough background events exist to justify
|
||||
this assumption. It follows from the central limit theorem that states, that for identical distributed random variables,
|
||||
their sum converges to a gaussian distribution. The significance therefore represents how good the signal can be
|
||||
distinguished from the background in units of the standard deviation of the background. As interval, a 10 % margin
|
||||
around the resonance nominal mass is chosen. The significance is then calculated for different selections on the
|
||||
discriminant of the two taggers and then plotted in dependence on the minimum resp. maximum allowed value of the
|
||||
discriminant to pass the selection for the deep boosted resp. the N-subjettiness tagger.
|
||||
error on the background so it will be calculated for the 2 TeV masspoint of the decay to qW where enough background
|
||||
events exist to justify this assumption, which follows from the central limit theorem [@CLT] that states, that for
|
||||
identical distributed random variables, their sum converges to a gaussian distribution. The significance represents how
|
||||
good the signal can be distinguished from the background in units of the standard deviation of the background. As
|
||||
interval, a 10 % margin around the resonance nominal mass is chosen. The significance is then calculated for different
|
||||
selections on the discriminant of the two taggers and then plotted in dependence on the minimum resp. maximum allowed
|
||||
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.
|
||||
|
||||
\begin{figure}
|
||||
|
|
@ -851,28 +852,27 @@ the data of the different years.
|
|||
\begin{minipage}{0.5\textwidth}
|
||||
\includegraphics{./figures/sig-tau.pdf}
|
||||
\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}
|
||||
\end{figure}
|
||||
|
||||
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
|
||||
it is very close to the edge where the significance drops very low and the higher the cut the less background will be
|
||||
left to calculate the cross section limits, especially at higher resonance masses, the slightly less strict cut is
|
||||
chosen.
|
||||
boosted cut is placed at $\ge 0.95$. For the DeepAK8 tagger, 0.97 would give a slightly higher significance but as it is
|
||||
very close to the edge where the significance drops very low and the higher the cut the less background will be left to
|
||||
calculate the cross section limits, especially at higher resonance masses, the slightly less strict cut is chosen.
|
||||
|
||||
For both taggers also a low purity category is introduced for high TeV regions. Using the cuts optimized for 2 TeV,
|
||||
there are very few background events left for higher resonance masses, but to reliably calculate cross section limits,
|
||||
those are needed. Therefore in the final cross section calculation, the two categories are combined to have a high
|
||||
signal sensitivity for all masspoints between 1.6 TeV and 7 TeV that were simulated. As low purity category for the
|
||||
N-subjettiness tagger, a cut at $0.35 < \tau_{21} < 0.75$ is used. For the deep boosted tagger the opposite cut from the
|
||||
high purity category is used: $VvsQCD < 0.95$.
|
||||
For both taggers also a low purity category is introduced. Using the cuts optimized for 2 TeV, there are very few
|
||||
background events left for higher resonance masses, but to reliably calculate cross section limits, those are needed.
|
||||
Therefore in the final cross section calculation, the two categories are combined to have a high signal sensitivity for
|
||||
all masspoints between 1.6 TeV and 7 TeV that were simulated. As low purity category for the N-subjettiness tagger, a
|
||||
cut at $0.35 < \tau_{21} < 0.75$ is used. For the DeepAK8 tagger the opposite cut from the high purity category is used:
|
||||
$VvsQCD < 0.95$.
|
||||
|
||||
\newpage
|
||||
|
||||
# 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
|
||||
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.
|
||||
|
|
@ -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 %
|
||||
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.
|
||||
Since no significant deviation from the Standard Model is found while looking for the resonance, the crossing of the
|
||||
theory line with the observed limit is calculated, to have a limit of mass up to which the existence of the q\* particle
|
||||
can be excluded. To find the uncertainty of this result, the crossing of the theory line plus, respectively minus, its
|
||||
uncertainty with the observed limit is also calculated.
|
||||
cross section limits expected, if the q\* predicted by [@QSTAR_THEORY] would exist. Since no significant deviation from
|
||||
the Standard Model is found while looking for the resonance, the crossing of the theory line with the observed limit is
|
||||
calculated, to have a limit of mass up to which the existence of the q\* particle can be excluded. To find the
|
||||
uncertainty of this result, the crossing of the theory line plus, respectively minus, its uncertainty with the observed
|
||||
limit is also calculated.
|
||||
|
||||
## 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
|
||||
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
|
||||
|
||||
# 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
|
||||
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
|
||||
region where theory and observed limit cross is very high compared to when using the N-subjettiness tagger. Therefore
|
||||
the two lines cross at lower resonance masses, which results in lower exclusion limits on the mass of the q\* particle
|
||||
causing the deep boosted tagger to perform worse than the N-subjettiness tagger in regards of establishing those limits
|
||||
as can be seen in [@tbl:res2016]. The table also shows the upper and lower limits on the mass found by calculating the
|
||||
crossing of the theory plus resp. minus its uncertainty. Due to the theory and the observed limits line being slowly
|
||||
falling in the high TeV region, even a small uncertainty of the theory can cause a high difference of the mass limit.
|
||||
limit. In [@fig:res2016dw,@fig:res2016dz] it can be seen, that the observed limit using the DeepAK8 tagger in the region
|
||||
where theory and observed limit cross is very high compared to when using the N-subjettiness tagger. Therefore the two
|
||||
lines cross at lower resonance masses, which results in lower exclusion limits on the mass of the q\* particle causing
|
||||
the DeepAK8 tagger to perform worse than the N-subjettiness tagger in regards of establishing those limits as can be
|
||||
seen in [@tbl:res2016]. The table also shows the upper and lower limits on the mass found by calculating the crossing of
|
||||
the theory plus resp. minus its uncertainty. Due to the theory and the observed limits line being slowly falling in the
|
||||
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}
|
||||
|
||||
| Decay | Tagger | Limit [TeV] | Upper Limit [TeV] | Lower Limit [TeV] |
|
||||
|-------|--------------|-------------|-------------------|-------------------|
|
||||
| qW | $\tau_{21}$ | 5.39 | 6.01 | 4.99 |
|
||||
| qW | deep boosted | 4.96 | 5.19 | 4.84 |
|
||||
| qZ | $\tau_{21}$ | 4.86 | 4.96 | 4.70 |
|
||||
| qZ | deep boosted | 4.62 | 4.71 | 4.49 |
|
||||
| Decay | Tagger | Limit [TeV] | Upper Limit [TeV] | Lower Limit [TeV] |
|
||||
|-------|-------------|-------------|-------------------|-------------------|
|
||||
| qW | $\tau_{21}$ | 5.39 | 6.01 | 4.99 |
|
||||
| qW | DeepAK8 | 4.96 | 5.19 | 4.84 |
|
||||
| qZ | $\tau_{21}$ | 4.86 | 4.96 | 4.70 |
|
||||
| qZ | DeepAK8 | 4.62 | 4.71 | 4.49 |
|
||||
|
||||
|
||||
\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]{%
|
||||
\label{fig:res2016tw}%
|
||||
\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}%
|
||||
\label{fig:res2016dw}}\\
|
||||
\subfloat[Decay to qZ, using N-subjettiness tagger]{%
|
||||
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_2016tau_13TeV.pdf}%
|
||||
\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}%
|
||||
\label{fig:res2016dz}}%
|
||||
\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}
|
||||
\includegraphics{./figures/results/prev_qZ.png}
|
||||
\end{minipage}
|
||||
\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}.}
|
||||
\caption{Previous results of the cross section limits for q\* decaying to qW (left) and q\* decaying to qZ (right)
|
||||
\cite{PREV_RESEARCH}.}
|
||||
\label{fig:prev}
|
||||
\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}
|
||||
|
||||
| Decay | Tagger | Limit [TeV] | Upper Limit [TeV] | Lower Limit [TeV] |
|
||||
|-------|--------------|-------------|-------------------|-------------------|
|
||||
| qW | $\tau_{21}$ | 6.00 | 6.26 | 5.74 |
|
||||
| qW | deep boosted | 6.11 | 6.31 | 5.39 |
|
||||
| qZ | $\tau_{21}$ | 5.49 | 5.76 | 5.29 |
|
||||
| qZ | deep boosted | 4.95 | 5.13 | 4.85 |
|
||||
| Decay | Tagger | Limit [TeV] | Upper Limit [TeV] | Lower Limit [TeV] |
|
||||
|-------|-------------|-------------|-------------------|-------------------|
|
||||
| qW | $\tau_{21}$ | 6.00 | 6.26 | 5.74 |
|
||||
| qW | DeepAK8 | 6.11 | 6.31 | 5.39 |
|
||||
| qZ | $\tau_{21}$ | 5.49 | 5.76 | 5.29 |
|
||||
| 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
|
||||
|
|
@ -1021,16 +1021,16 @@ what was concluded by the previous research [@PREV_RESEARCH].
|
|||
\subfloat[Decay to qW, using N-subjettiness tagger]{%
|
||||
\label{fig:resCombinedtw}%
|
||||
\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}%
|
||||
\label{fig:resCombineddw}}\\
|
||||
\subfloat[Decay to qZ, using N-subjettiness tagger]{%
|
||||
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_Combinedtau_13TeV.pdf}%
|
||||
\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}%
|
||||
\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.}
|
||||
\label{fig:resCombined}
|
||||
\end{figure}
|
||||
|
|
@ -1038,25 +1038,26 @@ tagger.}
|
|||
|
||||
## 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.
|
||||
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
|
||||
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
|
||||
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.
|
||||
|
||||
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 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
|
||||
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
|
||||
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}
|
||||
|
||||
|
||||
{#fig:comp_2018 width=70%}
|
||||
{#fig:comp_2018 width=55%}
|
||||
|
||||
\clearpage
|
||||
\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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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.
|
||||
|
||||
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
|
||||
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.
|
||||
Therefore, with an updated training, it is expected that the presented results can be further improved.
|
||||
The performance of the two taggers used have been compared and found to produce similar results. This was unexpected, as
|
||||
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
|
||||
|
||||
|
|
|
|||
BIN
thesis.pdf
BIN
thesis.pdf
Binary file not shown.
|
|
@ -41,7 +41,7 @@
|
|||
>
|
||||
]>
|
||||
<requests version="1.0">
|
||||
<internal package="biblatex" priority="9" active="0">
|
||||
<internal package="biblatex" priority="9" active="1">
|
||||
<generic>latex</generic>
|
||||
<provides type="dynamic">
|
||||
<file>thesis.bcf</file>
|
||||
|
|
|
|||
470
thesis.tex
470
thesis.tex
|
|
@ -78,7 +78,7 @@
|
|||
\usepackage{csquotes}
|
||||
\usepackage{abstract}
|
||||
\pagenumbering{gobble}
|
||||
\setlength{\parskip}{0.5em}
|
||||
\setlength{\parskip}{0.4em}
|
||||
\bibliographystyle{lucas_unsrt}
|
||||
\makeatletter
|
||||
\@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
|
||||
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
|
||||
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.
|
||||
|
||||
\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
|
||||
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
|
||||
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
|
||||
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.
|
||||
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 strange, and third, the top and bottom quark. A full list
|
||||
of particles of the standard model can be found in Fig.~\ref{fig:sm}.
|
||||
Furthermore, all fermions have an associated anti particle with reversed
|
||||
charge. Bound states of multiple quarks also exist and are called
|
||||
hadrons.
|
||||
The three quark generations consist of the up and down, the charm and
|
||||
strange, and the top and bottom quark. A full list of particles of the
|
||||
standard model can be found in Fig.~\ref{fig:sm}. Furthermore, all
|
||||
fermions have an associated anti particle with reversed charge. Bound
|
||||
states of multiple quarks also exist and are called hadrons.
|
||||
|
||||
\begin{figure}
|
||||
\hypertarget{fig:sm}{%
|
||||
\centering
|
||||
\includegraphics[width=0.5\textwidth,height=\textheight]{./figures/sm_wikipedia.pdf}
|
||||
\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}
|
||||
|
||||
|
|
@ -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
|
||||
\(\SI{37.80}{\per\femto\barn}\). 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 setup, some data have to be discarded. The amount of usable data is
|
||||
\(\SI{34.92}{\per\femto\barn}\), \(\SI{41.53}{\per\femto\barn}\) and
|
||||
\(\SI{59.74}{\per\femto\barn}\) for the years 2016, 2017 and 2018,
|
||||
totalling to \(\SI{137.19}{\per\femto\barn}\) of data.
|
||||
\(\SI{63.67}{\per\femto\barn}\) \autocite{CMS_LUMI}. The amount of data
|
||||
usable for research is \(\SI{35.92}{\per\femto\barn}\),
|
||||
\(\SI{41.53}{\per\femto\barn}\) and \(\SI{59.74}{\per\femto\barn}\) for
|
||||
the years 2016, 2017 and 2018, totalling to
|
||||
\(\SI{137.19}{\per\femto\barn}\) of data.
|
||||
|
||||
\begin{figure}
|
||||
\hypertarget{fig:cms_setup}{%
|
||||
|
|
@ -603,8 +602,8 @@ neutrinos.
|
|||
\centering
|
||||
\includegraphics[width=0.6\textwidth,height=\textheight]{./figures/cms_coordinates.png}
|
||||
\caption{Coordinate conventions of the CMS illustrating the use of
|
||||
\(\eta\) and \(\phi\). The Z axis is in beam direction. Taken from
|
||||
\autocite{COORD_PLOT}}\label{fig:cmscoords}
|
||||
\(\eta\) and \(\phi\). The Z axis is in beam direction
|
||||
\autocite{COORD_PLOT}.}\label{fig:cmscoords}
|
||||
}
|
||||
\end{figure}
|
||||
|
||||
|
|
@ -721,8 +720,8 @@ For this analysis, a radius of 0.8 is used.
|
|||
\includegraphics{./figures/antikt-comparision.png}
|
||||
\caption{Comparison of the \(k_t\), Cambridge/Aachen, SISCone and
|
||||
anti-\(k_t\) algorithms clustering a sample parton-level event with many
|
||||
random soft \enquote{ghosts}. Taken from
|
||||
\autocite{ANTIKT}}\label{fig:antiktcomparison}
|
||||
random soft \enquote{ghosts}
|
||||
\autocite{ANTIKT}.}\label{fig:antiktcomparison}
|
||||
}
|
||||
\end{figure}
|
||||
|
||||
|
|
@ -789,16 +788,16 @@ background processes, the 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 MadGraph
|
||||
and Pythia 8. Later on, also detector effects (like its limited
|
||||
resolution) are applied to make sure, they look like real data coming
|
||||
from the CMS detector.
|
||||
\autocite{MADGRAPH} and Pythia 8 \autocite{PYTHIA8}. Later on, also
|
||||
detector effects (like its limited resolution) are applied 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 \autocite{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 TeV. Because of the expected
|
||||
high mass, the signal width will be dominated by the resolution of the
|
||||
detector, not by the natural resonance width.
|
||||
\(\SI{1}{\pico\barn}\). The simulation was done using MadGraph
|
||||
\autocite{MADGRAPH} for eleven masspoints between 1.6 TeV and 7 TeV.
|
||||
Because of the expected high mass, the signal width will be dominated by
|
||||
the resolution of the detector, not by the natural resonance width.
|
||||
|
||||
The dijet invariant mass distribution of the QCD background is expected
|
||||
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.
|
||||
|
||||
A linear combination of the signal and background function is then
|
||||
fitted to a toy dataset with gaussian errors and a simulated signal
|
||||
cross section of \(\SI{1}{\per\pico\barn}\). The resulting coefficients
|
||||
of said combination then show the expected signal rate for the simulated
|
||||
cross section. An example of such a fit can be seen in
|
||||
Fig.~\ref{fig:cb_fit}. In this figure, a binning of 200 GeV is used for
|
||||
presentational purposes. The analysis itself is conducted using a 1 GeV
|
||||
binning. It can be seen that the fit works very well and therefore
|
||||
confirms the functions chosen 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 \(\pm\) 23 \(\si{\giga\eV}\) which is in very good agreement
|
||||
with the expected 3000 GeV mean. Those numbers clearly show that the
|
||||
method in use is able to successfully describe the simulated toy data.
|
||||
fitted to a toy dataset with gaussian errors obtained by adding
|
||||
simulated background and signal. The resulting coefficients of said
|
||||
combination then show the expected signal rate for the simulated signal
|
||||
cross section of \(\SI{1}{\pico\barn}\). An example of such a fit can be
|
||||
seen in Fig.~\ref{fig:cb_fit}. In this figure, a binning of 200 GeV is
|
||||
used for presentational purposes. The analysis itself is conducted using
|
||||
a 1 GeV binning. It can be seen that the fit works very well and
|
||||
therefore confirms the functions chosen 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 \(\pm\) 23 \(\si{\giga\eV}\) which is in very good
|
||||
agreement with the expected 3000 GeV mean. Those numbers clearly show
|
||||
that the method in use is able to successfully describe the simulated
|
||||
toy data.
|
||||
|
||||
\begin{figure}
|
||||
\hypertarget{fig:cb_fit}{%
|
||||
|
|
@ -909,6 +909,7 @@ events.
|
|||
\includegraphics{./figures/2016/v1_Njet_N_jets_stack.eps}
|
||||
\end{minipage}
|
||||
\begin{minipage}{\textwidth}
|
||||
\vspace{0.1cm}
|
||||
\centering\textbf{Comparison for the combined dataset}
|
||||
\end{minipage}
|
||||
\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
|
||||
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.~\ref{fig:deta} before and after the cut
|
||||
clearly shows, that the signal sensitivity was greatly improved by this
|
||||
cut.
|
||||
\(\Delta\eta \le 1.3\) is used. In the top two distributions of
|
||||
Fig.~\ref{fig:deta}, this cut is marked 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{minipage}{\textwidth}
|
||||
|
|
@ -967,7 +968,8 @@ cut.
|
|||
\begin{minipage}{0.5\textwidth}
|
||||
\includegraphics{./figures/combined/v1_Eta_invMass_stack.eps}
|
||||
\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 $\SI{137.19}{\per\femto\barn}$.
|
||||
}
|
||||
\label{fig:deta}
|
||||
|
|
@ -1043,8 +1045,8 @@ good agreement.
|
|||
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 were published by the CMS group. {[}cite
|
||||
todo{]}
|
||||
correctly. The corrections used were recommended by the CMS group for
|
||||
internal use \autocite{JEC}.
|
||||
|
||||
\begin{figure}
|
||||
\begin{minipage}{0.5\textwidth}
|
||||
|
|
@ -1075,8 +1077,8 @@ todo{]}
|
|||
\label{fig:data-mc}
|
||||
\end{figure}
|
||||
|
||||
\hypertarget{sideband}{%
|
||||
\subsubsection{Sideband}\label{sideband}}
|
||||
\hypertarget{sideband-region}{%
|
||||
\subsubsection{Sideband region}\label{sideband-region}}
|
||||
|
||||
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
|
||||
|
|
@ -1174,7 +1176,7 @@ 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 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
|
||||
will be determined by 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.
|
||||
|
|
@ -1196,9 +1198,8 @@ analysis.
|
|||
\centering
|
||||
\includegraphics[width=0.6\textwidth,height=\textheight]{./figures/deep_ak8.pdf}
|
||||
\caption{Comparison of tagger efficiencies, showing, between others, the
|
||||
DeepAK8-MD (which stands for mass decorrelated and is the one used for
|
||||
this research) and \(\tau_{21}\) tagger used in this analysis. Taken
|
||||
from \autocite{DEEP_BOOSTED}}\label{fig:ak8_eff}
|
||||
DeepAK8 and \(\tau_{21}\) tagger used in this analysis
|
||||
\autocite{DEEP_BOOSTED}.}\label{fig:ak8_eff}
|
||||
}
|
||||
\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
|
||||
development and validation.
|
||||
|
||||
In this thesis, the mass decorrelated version of the DeepAK8 tagger is
|
||||
used. It adds an additional mass predictor layer, that is trained to
|
||||
quantify how strongly the output of the non-decorrelated tagger is
|
||||
correlated to the mass of a particle. Its output is fed back to the
|
||||
network as a penalty so it avoids using features of the particles
|
||||
correlated to their mass. The result is a largely mass decorrelated
|
||||
tagger of heavy resonances, that doesn't introduce a bias in the jet
|
||||
mass shape. As can be seen in Fig.~\ref{fig:ak8_eff}, it performs not as
|
||||
good as the non-mass-decorrelated version, but still better than the
|
||||
other taggers it was compared to.
|
||||
In this thesis, the mass decorrelated version of the DeepAK8 tagger,
|
||||
called DeepAK8-MD but further referred to as only DeepAK8, is used. It
|
||||
adds an additional mass predictor layer, that is trained to quantify how
|
||||
strongly the output of the non-decorrelated tagger is correlated to the
|
||||
mass of a particle. Its output is fed back to the network as a penalty
|
||||
so it avoids using features of the particles correlated to their mass.
|
||||
The result is a largely mass decorrelated tagger of heavy resonances,
|
||||
that doesn't introduce a bias in the jet mass shape. As can be seen in
|
||||
Fig.~\ref{fig:ak8_eff}, it performs not 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 be caused by the
|
||||
decay of a vector boson. Therefore, using the same way to choose a
|
||||
candidate jet as for the N-subjettiness tagger, a selection is applied
|
||||
so that this candidate jet has a WvsQCD/ZvsQCD value greater than some
|
||||
value determined by the optimization presented next.
|
||||
The higher the discriminant value, called WvsQCD resp. ZvsQCD (further
|
||||
referred to as only VvsQCD), of the DeepAK8 tagger, the more likely is
|
||||
the jet to be caused by the decay of a vector boson. Therefore, using
|
||||
the same way to choose a candidate jet as for the N-subjettiness tagger,
|
||||
a selection is applied so that this candidate jet has a VvsQCD value
|
||||
greater than some value determined by the optimisation presented next.
|
||||
|
||||
\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
|
||||
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
|
||||
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 this assumption. It follows from the central limit theorem
|
||||
that states, that for identical distributed random variables, their sum
|
||||
converges to a gaussian distribution. The significance therefore
|
||||
represents how good the signal can be distinguished from the background
|
||||
in units of the standard deviation of the background. As interval, a 10
|
||||
\% margin around the resonance nominal mass is chosen. The significance
|
||||
is then calculated for different selections on the discriminant of the
|
||||
two taggers and then plotted in dependence on the minimum resp. maximum
|
||||
allowed value of the discriminant to pass the selection for the deep
|
||||
boosted resp. the N-subjettiness tagger.
|
||||
calculated for the 2 TeV masspoint of the decay to qW where enough
|
||||
background events exist to justify this assumption, which follows from
|
||||
the central limit theorem \autocite{CLT} that states, that for identical
|
||||
distributed random variables, their sum converges to a gaussian
|
||||
distribution. The significance represents how good the signal can be
|
||||
distinguished from the background in units of the standard deviation of
|
||||
the background. As interval, a 10 \% margin around the resonance nominal
|
||||
mass is chosen. The significance is then calculated for different
|
||||
selections on the discriminant of the two taggers and then plotted in
|
||||
dependence on the minimum resp. maximum allowed 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,
|
||||
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.
|
||||
|
||||
|
|
@ -1266,36 +1269,35 @@ different years.
|
|||
\begin{minipage}{0.5\textwidth}
|
||||
\includegraphics{./figures/sig-tau.pdf}
|
||||
\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}
|
||||
\end{figure}
|
||||
|
||||
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 it is very close to the edge where the
|
||||
significance drops very low and the higher the cut the less background
|
||||
will be left to calculate the cross section limits, especially at higher
|
||||
resonance masses, the slightly less strict cut is chosen.
|
||||
\(\ge 0.95\). For the DeepAK8 tagger, 0.97 would give a slightly higher
|
||||
significance but as it is very close to the edge where the significance
|
||||
drops very low and the higher the cut the less background will be left
|
||||
to calculate the cross section limits, especially at higher resonance
|
||||
masses, the slightly less strict cut is chosen.
|
||||
|
||||
For both taggers also a low purity category is introduced for high TeV
|
||||
regions. Using the cuts optimized for 2 TeV, there are very few
|
||||
background events left for higher resonance masses, but to reliably
|
||||
calculate cross section limits, those are needed. Therefore in the final
|
||||
cross section calculation, the two categories are combined to have a
|
||||
high signal sensitivity for all masspoints between 1.6 TeV and 7 TeV
|
||||
that were simulated. As low purity category for the N-subjettiness
|
||||
tagger, a cut at \(0.35 < \tau_{21} < 0.75\) is used. For the deep
|
||||
boosted tagger the opposite cut from the high purity category is used:
|
||||
\(VvsQCD < 0.95\).
|
||||
For both taggers also a low purity category is introduced. Using the
|
||||
cuts optimized for 2 TeV, there are very few background events left for
|
||||
higher resonance masses, but to reliably calculate cross section limits,
|
||||
those are needed. Therefore in the final cross section calculation, the
|
||||
two categories are combined to have a high signal sensitivity for all
|
||||
masspoints between 1.6 TeV and 7 TeV that were simulated. As low purity
|
||||
category for the N-subjettiness tagger, a cut at
|
||||
\(0.35 < \tau_{21} < 0.75\) is used. For the DeepAK8 tagger the opposite
|
||||
cut from the high purity category is used: \(VvsQCD < 0.95\).
|
||||
|
||||
\newpage
|
||||
|
||||
\hypertarget{sec:extr}{%
|
||||
\section{Signal extraction}\label{sec:extr}}
|
||||
|
||||
After the optimization, now the optimal selection for the N-subjettiness
|
||||
as well as the deep boosted tagger is found and applied to the simulated
|
||||
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.~\ref{sec:moa} is performed for all masspoints of the
|
||||
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
|
||||
reconstructed from the data.
|
||||
|
||||
Fourth, the uncertainty on the luminosity influence the normalization of
|
||||
the processes. Its value is 2.5 \% {[}cite todo{]}. \newpage
|
||||
Fourth, the uncertainty on the luminosity influences the normalization
|
||||
of the processes. Its value is 2.5 \% \autocite{LUMI_UNC}. \newpage
|
||||
|
||||
\hypertarget{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
|
||||
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
|
||||
seen, that the observed limit using the deep boosted tagger in the
|
||||
region where theory and observed limit cross is very high compared to
|
||||
when using the N-subjettiness tagger. Therefore the two lines cross at
|
||||
lower resonance masses, which results in lower exclusion limits on the
|
||||
mass of the q* particle causing the deep boosted tagger to perform worse
|
||||
than the N-subjettiness tagger in regards of establishing those limits
|
||||
as can be seen in Table~\ref{tbl:res2016}. The table also shows the
|
||||
upper and lower limits on the mass found by calculating the crossing of
|
||||
the theory plus resp. minus its uncertainty. Due to the theory and the
|
||||
observed limits line being slowly falling in the high TeV region, even a
|
||||
small uncertainty of the theory can cause a high difference of the mass
|
||||
limit.
|
||||
seen, that the observed limit using the DeepAK8 tagger in the region
|
||||
where theory and observed limit cross is very high compared to when
|
||||
using the N-subjettiness tagger. Therefore the two lines cross at lower
|
||||
resonance masses, which results in lower exclusion limits on the mass of
|
||||
the q* particle causing the DeepAK8 tagger to perform worse than the
|
||||
N-subjettiness tagger in regards of establishing those limits as can be
|
||||
seen in Table~\ref{tbl:res2016}. The table also shows the upper and
|
||||
lower limits on the mass found by calculating the crossing of the theory
|
||||
plus resp. minus its uncertainty. Due to the theory and the observed
|
||||
limits line being slowly falling in the high TeV region, even a small
|
||||
uncertainty of the theory can cause a high difference of the mass limit.
|
||||
|
||||
\hypertarget{tbl:res2016}{}
|
||||
\begin{longtable}[]{@{}lllll@{}}
|
||||
|
|
@ -1403,9 +1404,9 @@ Decay & Tagger & Limit {[}TeV{]} & Upper Limit {[}TeV{]} & Lower Limit
|
|||
\midrule
|
||||
\endhead
|
||||
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 & deep boosted & 4.62 & 4.71 & 4.49\tabularnewline
|
||||
qZ & DeepAK8 & 4.62 & 4.71 & 4.49\tabularnewline
|
||||
\bottomrule
|
||||
\end{longtable}
|
||||
|
||||
|
|
@ -1414,13 +1415,13 @@ qZ & deep boosted & 4.62 & 4.71 & 4.49\tabularnewline
|
|||
\subfloat[Decay to qW, using N-subjettiness tagger]{%
|
||||
\label{fig:res2016tw}%
|
||||
\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}%
|
||||
\label{fig:res2016dw}}\\
|
||||
\subfloat[Decay to qZ, using N-subjettiness tagger]{%
|
||||
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_2016tau_13TeV.pdf}%
|
||||
\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}%
|
||||
\label{fig:res2016dz}}%
|
||||
\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}
|
||||
\includegraphics{./figures/results/prev_qZ.png}
|
||||
\end{minipage}
|
||||
\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}.}
|
||||
\caption{Previous results of the cross section limits for q\* decaying to qW (left) and q\* decaying to qZ (right)
|
||||
\cite{PREV_RESEARCH}.}
|
||||
\label{fig:prev}
|
||||
\end{figure}
|
||||
|
||||
|
|
@ -1493,9 +1494,9 @@ Decay & Tagger & Limit {[}TeV{]} & Upper Limit {[}TeV{]} & Lower Limit
|
|||
\midrule
|
||||
\endhead
|
||||
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 & deep boosted & 4.95 & 5.13 & 4.85\tabularnewline
|
||||
qZ & DeepAK8 & 4.95 & 5.13 & 4.85\tabularnewline
|
||||
\bottomrule
|
||||
\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]{%
|
||||
\label{fig:resCombinedtw}%
|
||||
\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}%
|
||||
\label{fig:resCombineddw}}\\
|
||||
\subfloat[Decay to qZ, using N-subjettiness tagger]{%
|
||||
\includegraphics[width=0.5\textwidth]{./figures/results/brazilianFlag_QtoqZ_Combinedtau_13TeV.pdf}%
|
||||
\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}%
|
||||
\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.}
|
||||
\label{fig:resCombined}
|
||||
\end{figure}
|
||||
|
|
@ -1527,12 +1528,12 @@ tagger.}
|
|||
\hypertarget{comparison-of-taggers}{%
|
||||
\subsection{Comparison of taggers}\label{comparison-of-taggers}}
|
||||
|
||||
The results presented in Table~\ref{tbl:res2016} show, that the deep
|
||||
boosted tagger was not able to significantly improve the results
|
||||
compared to the N-subjettiness tagger. For further comparison, in
|
||||
The results presented in Table~\ref{tbl:res2016} show, that the DeepAK8
|
||||
tagger was not able to significantly improve the results compared to the
|
||||
N-subjettiness tagger. For further comparison, in
|
||||
Fig.~\ref{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
|
||||
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 higher significance in the
|
||||
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
|
||||
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
|
||||
using the deep boosted 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.
|
||||
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.
|
||||
|
||||
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 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
|
||||
have for several reasons. 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 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
|
||||
datasets, but those changes could not be incorporated into this thesis
|
||||
due to it not being possible to do this in a reasonable timeframe.
|
||||
\newpage
|
||||
|
||||
\begin{figure}
|
||||
\begin{minipage}{0.5\textwidth}
|
||||
|
|
@ -1573,10 +1574,9 @@ decay to qZ}
|
|||
\begin{figure}
|
||||
\hypertarget{fig:comp_2018}{%
|
||||
\centering
|
||||
\includegraphics[width=0.7\textwidth,height=\textheight]{./figures/limit_comp_2018.pdf}
|
||||
\caption{Comparision of deep boosted and N-subjettiness tagger in the
|
||||
high purity category using the data from year
|
||||
2018.}\label{fig:comp_2018}
|
||||
\includegraphics[width=0.55\textwidth,height=\textheight]{./figures/limit_comp_2018.pdf}
|
||||
\caption{Comparison of DeepAK8 and N-subjettiness tagger in the high
|
||||
purity category using the data from year 2018.}\label{fig:comp_2018}
|
||||
}
|
||||
\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
|
||||
dataset of \(\SI{35.92}{\per\femto\barn}\) was analysed, to be able to
|
||||
compare the results to previous research. Monte Carlo simulations were
|
||||
used to estimate the QCD background and signal.
|
||||
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 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
|
||||
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
|
||||
\(0.35 < \tau_{21} < 0.75\). These values were obtained by optimising
|
||||
for the highest possible significance of the signal.
|
||||
|
||||
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
|
||||
TeV resp. 4.7 TeV.
|
||||
|
||||
The DeepAK8 tagger performed worse than was expected. This can be
|
||||
explained with some training issues identified lately. Therefore, with
|
||||
an updated training, it is expected that the presented results can be
|
||||
further improved.
|
||||
The performance of the two taggers used have been compared and found to
|
||||
produce similar results. This was unexpected, as 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
|
||||
|
||||
|
|
@ -1635,6 +1639,7 @@ further improved.
|
|||
|
||||
\printbibliography
|
||||
|
||||
\newpage
|
||||
\appendix
|
||||
|
||||
\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
|
||||
\midrule
|
||||
\endhead
|
||||
1.6 & 0.10406 & 0.14720 & 0.07371 & 0.08165\tabularnewline
|
||||
1.8 & 0.07656 & 0.10800 & 0.05441 & 0.04114\tabularnewline
|
||||
2.0 & 0.05422 & 0.07605 & 0.03879 & 0.04043\tabularnewline
|
||||
2.5 & 0.02430 & 0.03408 & 0.01747 & 0.04052\tabularnewline
|
||||
3.0 & 0.01262 & 0.01775 & 0.00904 & 0.02109\tabularnewline
|
||||
3.5 & 0.00703 & 0.00992 & 0.00502 & 0.00399\tabularnewline
|
||||
4.0 & 0.00424 & 0.00603 & 0.00300 & 0.00172\tabularnewline
|
||||
4.5 & 0.00355 & 0.00478 & 0.00273 & 0.00249\tabularnewline
|
||||
5.0 & 0.00269 & 0.00357 & 0.00211 & 0.00240\tabularnewline
|
||||
6.0 & 0.00103 & 0.00160 & 0.00068 & 0.00062\tabularnewline
|
||||
7.0 & 0.00063 & 0.00105 & 0.00039 & 0.00086\tabularnewline
|
||||
1.6 & 0.10 & 0.15 & 0.074 & 0.082\tabularnewline
|
||||
1.8 & 0.077 & 0.11 & 0.054 & 0.041\tabularnewline
|
||||
2.0 & 0.054 & 0.076 & 0.039 & 0.040\tabularnewline
|
||||
2.5 & 0.024 & 0.034 & 0.017 & 0.041\tabularnewline
|
||||
3.0 & 0.013 & 0.018 & 0.009 & 0.021\tabularnewline
|
||||
3.5 & 0.0070 & 0.0099 & 0.005 & 0.004\tabularnewline
|
||||
4.0 & 0.0042 & 0.0060 & 0.003 & 0.0017\tabularnewline
|
||||
4.0 & 0.0042 & 0.0060 & 0.003 & 0.0017\tabularnewline
|
||||
4.5 & 0.0035 & 0.0048 & 0.0027 & 0.0025\tabularnewline
|
||||
5.0 & 0.0027 & 0.0036 & 0.0021 & 0.0024\tabularnewline
|
||||
6.0 & 0.0010 & 0.0016 & 0.00068 & 0.00062\tabularnewline
|
||||
7.0 & 0.00063 & 0.0010 & 0.00039 & 0.00086\tabularnewline
|
||||
\bottomrule
|
||||
\end{longtable}
|
||||
|
||||
|
|
@ -1681,17 +1687,17 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
|
|||
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
|
||||
\midrule
|
||||
\endhead
|
||||
1.6 & 0.17750 & 0.25179 & 0.12572 & 0.38242\tabularnewline
|
||||
1.8 & 0.11125 & 0.15870 & 0.07826 & 0.11692\tabularnewline
|
||||
2.0 & 0.08188 & 0.11549 & 0.05799 & 0.09528\tabularnewline
|
||||
2.5 & 0.03328 & 0.04668 & 0.02373 & 0.03653\tabularnewline
|
||||
3.0 & 0.01648 & 0.02338 & 0.01181 & 0.01108\tabularnewline
|
||||
3.5 & 0.00840 & 0.01195 & 0.00593 & 0.00683\tabularnewline
|
||||
4.0 & 0.00459 & 0.00666 & 0.00322 & 0.00342\tabularnewline
|
||||
4.5 & 0.00276 & 0.00412 & 0.00190 & 0.00366\tabularnewline
|
||||
5.0 & 0.00177 & 0.00271 & 0.00118 & 0.00401\tabularnewline
|
||||
6.0 & 0.00110 & 0.00175 & 0.00071 & 0.00155\tabularnewline
|
||||
7.0 & 0.00065 & 0.00108 & 0.00041 & 0.00108\tabularnewline
|
||||
1.6 & 0.18 & 0.25 & 0.13 & 0.38\tabularnewline
|
||||
1.8 & 0.11 & 0.16 & 0.078 & 0.12\tabularnewline
|
||||
2.0 & 0.082 & 0.12 & 0.058 & 0.095\tabularnewline
|
||||
2.5 & 0.033 & 0.047 & 0.024 & 0.037\tabularnewline
|
||||
3.0 & 0.016 & 0.023 & 0.012 & 0.011\tabularnewline
|
||||
3.5 & 0.0084 & 0.012 & 0.0059 & 0.0068\tabularnewline
|
||||
4.0 & 0.0046 & 0.0067 & 0.0032 & 0.0034\tabularnewline
|
||||
4.5 & 0.0028 & 0.0041 & 0.0019 & 0.0037\tabularnewline
|
||||
5.0 & 0.0018 & 0.0027 & 0.0012 & 0.0040\tabularnewline
|
||||
6.0 & 0.0011 & 0.0017 & 0.00071 & 0.0016\tabularnewline
|
||||
7.0 & 0.00065 & 0.0011 & 0.00041 & 0.0011\tabularnewline
|
||||
\bottomrule
|
||||
\end{longtable}
|
||||
|
||||
|
|
@ -1708,16 +1714,16 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
|
|||
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
|
||||
\midrule
|
||||
\endhead
|
||||
1.6 & 0.08687 & 0.12254 & 0.06174 & 0.06987\tabularnewline
|
||||
1.8 & 0.06719 & 0.09477 & 0.04832 & 0.03424\tabularnewline
|
||||
2.0 & 0.04734 & 0.06640 & 0.03405 & 0.03310\tabularnewline
|
||||
2.5 & 0.01867 & 0.02619 & 0.01343 & 0.03214\tabularnewline
|
||||
3.0 & 0.01043 & 0.01463 & 0.00744 & 0.01773\tabularnewline
|
||||
3.5 & 0.00596 & 0.00840 & 0.00426 & 0.00347\tabularnewline
|
||||
4.0 & 0.00353 & 0.00500 & 0.00250 & 0.00140\tabularnewline
|
||||
4.5 & 0.00233 & 0.00335 & 0.00164 & 0.00181\tabularnewline
|
||||
5.0 & 0.00157 & 0.00231 & 0.00110 & 0.00188\tabularnewline
|
||||
6.0 & 0.00082 & 0.00126 & 0.00054 & 0.00049\tabularnewline
|
||||
1.6 & 0.087 & 0.12 & 0.062 & 0.07\tabularnewline
|
||||
1.8 & 0.067 & 0.095 & 0.048 & 0.034\tabularnewline
|
||||
2.0 & 0.047 & 0.066 & 0.034 & 0.033\tabularnewline
|
||||
2.5 & 0.019 & 0.026 & 0.013 & 0.032\tabularnewline
|
||||
3.0 & 0.010 & 0.015 & 0.0074 & 0.018\tabularnewline
|
||||
3.5 & 0.0060 & 0.0084 & 0.0043 & 0.0035\tabularnewline
|
||||
4.0 & 0.0035 & 0.0050 & 0.0025 & 0.0014\tabularnewline
|
||||
4.5 & 0.0023 & 0.0034 & 0.0016 & 0.0018\tabularnewline
|
||||
5.0 & 0.0016 & 0.0023 & 0.0011 & 0.0019\tabularnewline
|
||||
6.0 & 0.00082 & 0.0013 & 0.00054 & 0.00049\tabularnewline
|
||||
7.0 & 0.00050 & 0.00083 & 0.00031 & 0.00066\tabularnewline
|
||||
\bottomrule
|
||||
\end{longtable}
|
||||
|
|
@ -1735,17 +1741,17 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
|
|||
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
|
||||
\midrule
|
||||
\endhead
|
||||
1.6 & 0.16687 & 0.23805 & 0.11699 & 0.35999\tabularnewline
|
||||
1.8 & 0.12750 & 0.17934 & 0.09138 & 0.12891\tabularnewline
|
||||
2.0 & 0.09062 & 0.12783 & 0.06474 & 0.09977\tabularnewline
|
||||
2.5 & 0.03391 & 0.04783 & 0.02422 & 0.03754\tabularnewline
|
||||
3.0 & 0.01781 & 0.02513 & 0.01277 & 0.01159\tabularnewline
|
||||
3.5 & 0.00949 & 0.01346 & 0.00678 & 0.00741\tabularnewline
|
||||
4.0 & 0.00494 & 0.00711 & 0.00349 & 0.00362\tabularnewline
|
||||
4.5 & 0.00293 & 0.00429 & 0.00203 & 0.00368\tabularnewline
|
||||
5.0 & 0.00188 & 0.00284 & 0.00127 & 0.00426\tabularnewline
|
||||
6.0 & 0.00102 & 0.00161 & 0.00066 & 0.00155\tabularnewline
|
||||
7.0 & 0.00053 & 0.00085 & 0.00034 & 0.00085\tabularnewline
|
||||
1.6 & 0.15 & 0.22 & 0.11 & 0.33\tabularnewline
|
||||
1.8 & 0.10 & 0.14 & 0.072 & 0.085\tabularnewline
|
||||
2.0 & 0.077 & 0.11 & 0.056 & 0.064\tabularnewline
|
||||
2.5 & 0.027 & 0.038 & 0.019 & 0.041\tabularnewline
|
||||
3.0 & 0.015 & 0.021 & 0.010 & 0.0087\tabularnewline
|
||||
3.5 & 0.0084 & 0.012 & 0.006 & 0.0066\tabularnewline
|
||||
4.0 & 0.0049 & 0.0071 & 0.0035 & 0.0045\tabularnewline
|
||||
4.5 & 0.0032 & 0.0046 & 0.0022 & 0.0026\tabularnewline
|
||||
5.0 & 0.0022 & 0.0033 & 0.0015 & 0.0041\tabularnewline
|
||||
6.0 & 0.0012 & 0.0019 & 0.00081 & 0.0018\tabularnewline
|
||||
7.0 & 0.00057 & 0.00092 & 0.00037 & 0.00093\tabularnewline
|
||||
\bottomrule
|
||||
\end{longtable}
|
||||
|
||||
|
|
@ -1762,15 +1768,15 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
|
|||
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
|
||||
\midrule
|
||||
\endhead
|
||||
1.6 & 0.05703 & 0.07999 & 0.04088 & 0.03366\tabularnewline
|
||||
1.8 & 0.03953 & 0.05576 & 0.02833 & 0.04319\tabularnewline
|
||||
2.0 & 0.02844 & 0.03989 & 0.02045 & 0.04755\tabularnewline
|
||||
2.5 & 0.01270 & 0.01781 & 0.00913 & 0.01519\tabularnewline
|
||||
3.0 & 0.00658 & 0.00923 & 0.00473 & 0.01218\tabularnewline
|
||||
3.5 & 0.00376 & 0.00529 & 0.00269 & 0.00474\tabularnewline
|
||||
4.0 & 0.00218 & 0.00309 & 0.00156 & 0.00114\tabularnewline
|
||||
4.5 & 0.00132 & 0.00188 & 0.00094 & 0.00068\tabularnewline
|
||||
5.0 & 0.00084 & 0.00122 & 0.00060 & 0.00059\tabularnewline
|
||||
1.6 & 0.057 & 0.08 & 0.041 & 0.034\tabularnewline
|
||||
1.8 & 0.040 & 0.056 & 0.028 & 0.043\tabularnewline
|
||||
2.0 & 0.028 & 0.040 & 0.020 & 0.048\tabularnewline
|
||||
2.5 & 0.013 & 0.018 & 0.0091 & 0.015\tabularnewline
|
||||
3.0 & 0.0066 & 0.0092 & 0.0047 & 0.012\tabularnewline
|
||||
3.5 & 0.0038 & 0.0053 & 0.0027 & 0.0047\tabularnewline
|
||||
4.0 & 0.0022 & 0.0031 & 0.0016 & 0.0011\tabularnewline
|
||||
4.5 & 0.0013 & 0.0019 & 0.00094 & 0.00068\tabularnewline
|
||||
5.0 & 0.00084 & 0.0012 & 0.00060 & 0.00059\tabularnewline
|
||||
6.0 & 0.00044 & 0.00066 & 0.00030 & 0.00041\tabularnewline
|
||||
7.0 & 0.00022 & 0.00036 & 0.00014 & 0.00043\tabularnewline
|
||||
\bottomrule
|
||||
|
|
@ -1789,15 +1795,15 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
|
|||
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
|
||||
\midrule
|
||||
\endhead
|
||||
1.6 & 0.06656 & 0.09495 & 0.04698 & 0.12374\tabularnewline
|
||||
1.8 & 0.04281 & 0.06141 & 0.03001 & 0.05422\tabularnewline
|
||||
2.0 & 0.03297 & 0.04650 & 0.02363 & 0.04658\tabularnewline
|
||||
2.5 & 0.01328 & 0.01868 & 0.00950 & 0.01109\tabularnewline
|
||||
3.0 & 0.00650 & 0.00917 & 0.00464 & 0.00502\tabularnewline
|
||||
3.5 & 0.00338 & 0.00479 & 0.00241 & 0.00408\tabularnewline
|
||||
4.0 & 0.00182 & 0.00261 & 0.00129 & 0.00127\tabularnewline
|
||||
4.5 & 0.00107 & 0.00156 & 0.00074 & 0.00123\tabularnewline
|
||||
5.0 & 0.00068 & 0.00102 & 0.00046 & 0.00149\tabularnewline
|
||||
1.6 & 0.067 & 0.095 & 0.047 & 0.12\tabularnewline
|
||||
1.8 & 0.043 & 0.061 & 0.030 & 0.054\tabularnewline
|
||||
2.0 & 0.033 & 0.047 & 0.024 & 0.047\tabularnewline
|
||||
2.5 & 0.013 & 0.019 & 0.0095 & 0.011\tabularnewline
|
||||
3.0 & 0.0065 & 0.0092 & 0.0046 & 0.0050\tabularnewline
|
||||
3.5 & 0.0034 & 0.0048 & 0.0024 & 0.0041\tabularnewline
|
||||
4.0 & 0.0018 & 0.0026 & 0.0013 & 0.0013\tabularnewline
|
||||
4.5 & 0.0011 & 0.0016 & 0.00074 & 0.0012\tabularnewline
|
||||
5.0 & 0.00068 & 0.0010 & 0.00046 & 0.0015\tabularnewline
|
||||
6.0 & 0.00038 & 0.00060 & 0.00024 & 0.00034\tabularnewline
|
||||
7.0 & 0.00021 & 0.00035 & 0.00013 & 0.00046\tabularnewline
|
||||
\bottomrule
|
||||
|
|
@ -1816,15 +1822,15 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
|
|||
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
|
||||
\midrule
|
||||
\endhead
|
||||
1.6 & 0.05125 & 0.07188 & 0.03667 & 0.02993\tabularnewline
|
||||
1.8 & 0.03547 & 0.04989 & 0.02551 & 0.03614\tabularnewline
|
||||
2.0 & 0.02523 & 0.03539 & 0.01815 & 0.04177\tabularnewline
|
||||
2.5 & 0.01059 & 0.01485 & 0.00761 & 0.01230\tabularnewline
|
||||
3.0 & 0.00576 & 0.00808 & 0.00412 & 0.01087\tabularnewline
|
||||
3.5 & 0.00327 & 0.00460 & 0.00234 & 0.00425\tabularnewline
|
||||
4.0 & 0.00190 & 0.00269 & 0.00136 & 0.00097\tabularnewline
|
||||
4.5 & 0.00119 & 0.00168 & 0.00084 & 0.00059\tabularnewline
|
||||
5.0 & 0.00077 & 0.00110 & 0.00054 & 0.00051\tabularnewline
|
||||
1.6 & 0.051 & 0.072 & 0.037 & 0.030\tabularnewline
|
||||
1.8 & 0.035 & 0.050 & 0.026 & 0.036\tabularnewline
|
||||
2.0 & 0.025 & 0.035 & 0.018 & 0.042\tabularnewline
|
||||
2.5 & 0.011 & 0.015 & 0.0076 & 0.012\tabularnewline
|
||||
3.0 & 0.0058 & 0.0081 & 0.0041 & 0.011\tabularnewline
|
||||
3.5 & 0.0033 & 0.0046 & 0.0023 & 0.0042\tabularnewline
|
||||
4.0 & 0.0019 & 0.0027 & 0.0014 & 0.00097\tabularnewline
|
||||
4.5 & 0.0012 & 0.0017 & 0.00084 & 0.00059\tabularnewline
|
||||
5.0 & 0.00077 & 0.0011 & 0.00054 & 0.00051\tabularnewline
|
||||
6.0 & 0.00039 & 0.00057 & 0.00026 & 0.00036\tabularnewline
|
||||
7.0 & 0.00019 & 0.00031 & 0.00013 & 0.00036\tabularnewline
|
||||
\bottomrule
|
||||
|
|
@ -1843,18 +1849,36 @@ Mass {[}TeV{]} & Exp. limit {[}pb{]} & Upper limit {[}pb{]} & Lower
|
|||
limit {[}pb{]} & Obs. limit {[}pb{]}\tabularnewline
|
||||
\midrule
|
||||
\endhead
|
||||
1.6 & 0.07719 & 0.10949 & 0.05467 & 0.14090\tabularnewline
|
||||
1.8 & 0.05297 & 0.07493 & 0.03752 & 0.06690\tabularnewline
|
||||
2.0 & 0.03875 & 0.05466 & 0.02768 & 0.05855\tabularnewline
|
||||
2.5 & 0.01512 & 0.02126 & 0.01080 & 0.01160\tabularnewline
|
||||
3.0 & 0.00773 & 0.01088 & 0.00554 & 0.00548\tabularnewline
|
||||
3.5 & 0.00400 & 0.00565 & 0.00285 & 0.00465\tabularnewline
|
||||
4.0 & 0.00211 & 0.00301 & 0.00149 & 0.00152\tabularnewline
|
||||
4.5 & 0.00118 & 0.00172 & 0.00082 & 0.00128\tabularnewline
|
||||
5.0 & 0.00073 & 0.00108 & 0.00050 & 0.00161\tabularnewline
|
||||
6.0 & 0.00039 & 0.00060 & 0.00025 & 0.00036\tabularnewline
|
||||
7.0 & 0.00021 & 0.00034 & 0.00013 & 0.00045\tabularnewline
|
||||
1.6 & 0.067 & 0.095 & 0.047 & 0.095\tabularnewline
|
||||
1.8 & 0.044 & 0.063 & 0.032 & 0.048\tabularnewline
|
||||
2.0 & 0.032 & 0.045 & 0.023 & 0.045\tabularnewline
|
||||
2.5 & 0.012 & 0.017 & 0.0088 & 0.013\tabularnewline
|
||||
3.0 & 0.0064 & 0.009 & 0.0046 & 0.0032\tabularnewline
|
||||
3.5 & 0.0036 & 0.0051 & 0.0026 & 0.0039\tabularnewline
|
||||
4.0 & 0.0021 & 0.0029 & 0.0015 & 0.0027\tabularnewline
|
||||
4.5 & 0.0013 & 0.0018 & 0.00088 & 0.00094\tabularnewline
|
||||
5.0 & 0.00083 & 0.0012 & 0.00057 & 0.00150\tabularnewline
|
||||
6.0 & 0.00046 & 0.00072 & 0.00031 & 0.00043\tabularnewline
|
||||
7.0 & 0.00023 & 0.00037 & 0.00015 & 0.00049\tabularnewline
|
||||
\bottomrule
|
||||
\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}
|
||||
|
|
|
|||
20
thesis.toc
20
thesis.toc
|
|
@ -10,10 +10,10 @@
|
|||
\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 {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 {3.2.4}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 {3.2.7}The Trigger system}{9}{subsubsection.3.2.7}%
|
||||
\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 {subsection}{\numberline {5.1}Preselection}{13}{subsection.5.1}%
|
||||
\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 {subsection}{\numberline {6.1}N-Subjettiness}{20}{subsection.6.1}%
|
||||
\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 {subsection}{\numberline {7.1}Systematic Uncertainties}{24}{subsection.7.1}%
|
||||
\contentsline {section}{\numberline {8}Results}{26}{section.8}%
|
||||
\contentsline {subsection}{\numberline {8.1}Partial dataset}{26}{subsection.8.1}%
|
||||
\contentsline {subsubsection}{\numberline {8.1.1}Comparison with existing results}{26}{subsubsection.8.1.1}%
|
||||
\contentsline {subsection}{\numberline {8.2}Combined dataset}{28}{subsection.8.2}%
|
||||
\contentsline {subsection}{\numberline {8.3}Comparison of taggers}{30}{subsection.8.3}%
|
||||
\contentsline {section}{\numberline {9}Summary}{32}{section.9}%
|
||||
\contentsline {section}{\numberline {8}Results}{25}{section.8}%
|
||||
\contentsline {subsection}{\numberline {8.1}Partial dataset}{25}{subsection.8.1}%
|
||||
\contentsline {subsubsection}{\numberline {8.1.1}Comparison with existing results}{25}{subsubsection.8.1.1}%
|
||||
\contentsline {subsection}{\numberline {8.2}Combined dataset}{27}{subsection.8.2}%
|
||||
\contentsline {subsection}{\numberline {8.3}Comparison of taggers}{29}{subsection.8.3}%
|
||||
\contentsline {section}{\numberline {9}Summary}{31}{section.9}%
|
||||
\contentsline {section}{\numberline {A}Expected and observed cross section limits}{34}{appendix.A}%
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user