Learning new physics from an imperfect machine - Archive ouverte HAL
Article Dans Une Revue European Physical Journal C: Particles and Fields Année : 2022

Learning new physics from an imperfect machine

Gaia Grosso
  • Fonction : Auteur
Maurizio Pierini
  • Fonction : Auteur
Andrea Wulzer
  • Fonction : Auteur
Marco Zanetti
  • Fonction : Auteur

Résumé

We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties as nuisance parameters for hypothesis testing that is routinely employed in high-energy physics. After presenting the conceptual foundations of our method, we first illustrate all aspects of its implementation and extensively study its performances on a toy one-dimensional problem. We then show how to implement it in a multivariate setup by studying the impact of two typical sources of experimental uncertainties in two-body final states at the LHC.

Dates et versions

hal-03477010 , version 1 (13-12-2021)

Identifiants

Citer

Raffaele Tito d'Agnolo, Gaia Grosso, Maurizio Pierini, Andrea Wulzer, Marco Zanetti. Learning new physics from an imperfect machine. European Physical Journal C: Particles and Fields, 2022, 82 (3), pp.275. ⟨10.1140/epjc/s10052-022-10226-y⟩. ⟨hal-03477010⟩
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