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A method for approximating optimal statistical significances with machine-learned likelihoods

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A method for approximating optimal statistical significances with machine-learned likelihoods

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dc.contributor.author	Arganda Carreras, Ernesto
dc.contributor.author	Marcano Imaz, Xavier
dc.contributor.author	Martín Lozano, Víctor
dc.contributor.author	Medina, Anibal
dc.contributor.author	Pérez, Andrés
dc.contributor.author	Szewc, M.
dc.contributor.author	Szynkman, Alejandro
dc.date.accessioned	2023-05-30T16:19:42Z
dc.date.available	2023-05-30T16:19:42Z
dc.date.issued	2022
dc.identifier.citation	Arganda Carreras, Ernesto Marcano Imaz, Xavier Martín Lozano, Víctor Medina, Anibal Pérez, Andrés Szewc, M. Szynkman, Alejandro 2022 A method for approximating optimal statistical significances with machine-learned likelihoods European Physical Journal C 82 993
dc.identifier.uri	https://hdl.handle.net/10550/87128
dc.description.abstract	Machine-learning techniques have become fundamental in high-energy physics and, for new physics searches, it is crucial to know their performance in terms of experimental sensitivity, understood as the statistical significance of the signal-plus-background hypothesis over the background-only one. We present here a simple method that combines the power of current machine-learning techniques to face high-dimensional data with the likelihood-based inference tests used in traditional analyses, which allows us to estimate the sensitivity for both discovery and exclusion limits through a single parameter of interest, the signal strength. Based on supervised learning techniques, it can perform well also with high-dimensional data, when traditional techniques cannot. We apply the method to a toy model first, so we can explore its potential, and then to a LHC study of new physics particles in dijet final states. Considering as the optimal statistical significance the one we would obtain if the true generative functions were known, we show that our method provides a better approximation than the usual naive counting experimental results.
dc.language.iso	eng
dc.relation.ispartof	European Physical Journal C, 2022, vol. 82, num. 993
dc.subject	Física
dc.title	A method for approximating optimal statistical significances with machine-learned likelihoods
dc.type	journal article
dc.date.updated	2023-05-30T16:19:42Z
dc.identifier.doi	10.1140/epjc/s10052-022-10944-3
dc.identifier.idgrec	159138
dc.rights.accessRights	open access