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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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Bibliographic Details
Main Authors: Butter, Anja (Author) , Heimel, Theo (Author) , Martini, Till (Author) , Peitzsch, Sascha (Author) , Plehn, Tilman (Author)
Format: Article (Journal)
Language:English
Published: 14-09-2023
In: SciPost physics
Year: 2023, Volume: 15, Issue: 3, Pages: 1-24
ISSN:2542-4653
DOI:10.21468/SciPostPhys.15.3.094
Online Access: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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Author Notes:Anja Butter, Theo Heimel, Till Martini, Sascha Peitzsch and Tilman Plehn
Description
Summary: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.
Item Description:Veröffentlicht: 14. September 2023
Gesehen am 28.11.2023
Physical Description:Online Resource
ISSN:2542-4653
DOI:10.21468/SciPostPhys.15.3.094

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