Jet tagging made easy
Entity
UAM. Departamento de Física TeóricaPublisher
SpringerOpenDate
2020-06-13Citation
10.1140/epjc/s10052-020-8082-8
European Physical Journal C 80.6 (2020): 530
ISSN
1434-6044 (print); 1434-6052 (online)DOI
10.1140/epjc/s10052-020-8082-8Project
Gobierno de España. SEV-2016-0597Editor's Version
https://doi.org/10.1140/epjc/s10052-020-8082-8Subjects
Substructure; Gluons; Top Quark; FísicaRights
© The Author(s) 2020Abstract
We develop taggers for multi-pronged jets that are simple functions of jet substructure (so-called ‘subjettiness’) variables. These taggers can be approximately decorrelated from the jet mass in a quite simple way. Specifically, we use a Logistic Regression Design (LoRD) which, even being one of the simplest machine learning classifiers, shows a performance which surpasses that of simple variables used by the ATLAS and CMS Collaborations and is not far from more complex models based on neural networks. Contrary to the latter, our method allows for an easy implementation of tagging tasks by providing a simple and interpretable analytical formula with already optimised parameters
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Google Scholar:Aguilar Saavedra, Juan Antonio
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Zaldívar Montero, Bryan
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