Regression Deep Neural Networks for top-quark-pair resonance searches in the dilepton channel

Guerrieri, G. (2022) Regression Deep Neural Networks for top-quark-pair resonance searches in the dilepton channel. Il nuovo cimento C, 45 (5). pp. 1-4. ISSN 1826-9885

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Abstract

Several Beyond Standard Model (BSM) theories predict the existence of new massive particles decaying to pairs of top quarks, tt¯. In this concept work, the key observable for such resonance searches, the top-pair system invariant mass, mtt¯, is reconstructed by training a deep neural network on a sample of simulated tt¯ events. A regression task is then performed on both tt¯ and Z signal events, using mtt¯ as output parameter. The comparison between this machine learning approach and more traditional system reconstruction techniques highlights a tangible improvement in the ability to correctly reconstruct and resolve a TeV-scale tt¯ resonance peak.

Item Type:	Article
Subjects:	500 Scienze naturali e Matematica > 530 Fisica
Depositing User:	Marina Spanti
Date Deposited:	06 Sep 2022 12:45
Last Modified:	06 Sep 2022 12:45
URI:	http://eprints.bice.rm.cnr.it/id/eprint/22014

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