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HYTREES: combining matrix elements and parton shower for hypothesis testing

Prestel, Stefan; Spannowsky, Michael

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Authors

Stefan Prestel



Abstract

We present a new way of performing hypothesis tests on scattering data, by means of a perturbatively calculable classifier. This classifier exploits the “history tree” of how the measured data point might have evolved out of any simpler (reconstructed) points along classical paths, while explicitly keeping quantum–mechanical interference effects by copiously employing complete leading-order matrix elements. This approach extends the standard Matrix Element Method to an arbitrary number of final state objects and to exclusive final states where reconstructed objects can be collinear or soft. We have implemented this method into the standalone package hytrees and have applied it to Higgs boson production in association with two jets, with subsequent decay into photons. hytrees allows to construct an optimal classifier to discriminate this process from large Standard Model backgrounds. It further allows to find the most sensitive kinematic regions that contribute to the classification.

Citation

Prestel, S., & Spannowsky, M. (2019). HYTREES: combining matrix elements and parton shower for hypothesis testing. The European Physical Journal C, 79(7), Article 546. https://doi.org/10.1140/epjc/s10052-019-7030-y

Journal Article Type Article
Acceptance Date Jun 8, 2019
Online Publication Date Jun 28, 2019
Publication Date Jun 28, 2019
Deposit Date Jul 16, 2019
Publicly Available Date Mar 29, 2024
Journal European Physical Journal C: Particles and Fields
Print ISSN 1434-6044
Electronic ISSN 1434-6052
Publisher SpringerOpen
Peer Reviewed Peer Reviewed
Volume 79
Issue 7
Article Number 546
DOI https://doi.org/10.1140/epjc/s10052-019-7030-y

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
© The Author(s) 2019



Open Access
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.





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