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Accès ouvert déclaré 2021 conference-paper

A Machine Learning approach to the EFT re-interpretation of the WZjj fully leptonic electroweak production

0Citations signalées, ce qui n’est pas une note de qualité
4Institutions déclarées
2Pays d’affiliation déclarés

Rattachement africain : gr, cn. Niveau de preuve : code pays fourni par la source.

Le résumé fourni par la source

Abstract In this paper we study the use of Machine Learning techniques to set constraints on indirect signatures of physics beyond the Standard Model in Vector Boson Scattering (VBS), in the electroweak (EWK) production of self-interacting W ± Z bosons in association with two jets. The WZ fully leptonic channel has been extensively studied by the ATLAS Collaboration at the LHC and we are about to provide results using the full Run 2 data corresponding to an integrated luminosity of 139fb −1. The EWK production of the WZ in association with two jets has been already observed at 36fb −1 with an observed significance of 5.3 standard deviations. A factor of four increase in the integrated luminosity provides an opportunity to check for deviations from the Standard Model (SM) predictions, in particular for model independent, indirect searches for New Physics. Such searches can be realized in the context of an extension of the SM in terms of an Effective Field Theory (EFT) formalism, providing a way to quantify possible deviations from the Standard Model. The EFT Lagrangian besides the Standard Model terms comprises contributions from higher dimension operators, their effect being determined by the strength of their corresponding parameters (Wilson coefficients scaled to the appropriate power of Λ, indicating the scale of the appearance of New Physics). In this paper an attempt is made to search for New Physics effects in the WZjj production, using state-of-the-art machine learning models where diverse network architectures are effectively combined into ensembles trained on the outcomes of base learners maximizing performance. The base learners are trained to identify pure WZjj signal events originating from the effect of EFT operators, from WZjj background events originating from strong (QCD) or EWK WZjj processes. We investigate the utilization of the ensemble model response in estimating the sensitivity of WZjj events in some of the dimension-8 EFT operators and compare the results to sensitive kinematic variables traditionally used to constrain the EFT operator effects.

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Le contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
A Machine Learning approach to the EFT re-interpretation of the WZjj fully leptonic electroweak production
Date Crossref
01/11/2021
Éditeur
IOP Publishing
Type
journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.

Les institutions déclarées

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Les sujets associés

Particle physics theoretical and experimental studiesHigh-Energy Particle Collisions ResearchQuantum Chromodynamics and Particle Interactions

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