Two invertible networks for the matrix element method
The matrix element method is widely considered the ultimate LHC inference tool for small event numbers. We show how a combination of two conditional generative neural networks encodes the QCD radiation and detector effects without any simplifying assumptions, while keeping the computation of likelih...
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| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
14-09-2023
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| In: |
SciPost physics
Year: 2023, Jahrgang: 15, Heft: 3, Pages: 1-24 |
| ISSN: | 2542-4653 |
| DOI: | 10.21468/SciPostPhys.15.3.094 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.21468/SciPostPhys.15.3.094 Verlag, lizenzpflichtig, Volltext: https://scipost.org/10.21468/SciPostPhys.15.3.094 
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| Verfasserangaben: | Anja Butter, Theo Heimel, Till Martini, Sascha Peitzsch and Tilman Plehn |
MARC
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| 520 | |a The matrix element method is widely considered the ultimate LHC inference tool for small event numbers. We show how a combination of two conditional generative neural networks encodes the QCD radiation and detector effects without any simplifying assumptions, while keeping the computation of likelihoods for individual events numerically efficient. We illustrate our approach for the CP-violating phase of the top Yukawa coupling in associated Higgs and single-top production. Currently, the limiting factor for the precision of our approach is jet combinatorics. | ||
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