Jet tagging made easy
Entidad
UAM. Departamento de Física TeóricaEditor
SpringerOpenFecha de edición
2020-06-13Cita
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/s10052-020-8082-8Proyecto
Gobierno de España. SEV-2016-0597Versión del editor
https://doi.org/10.1140/epjc/s10052-020-8082-8Materias
Substructure; Gluons; Top Quark; FísicaDerechos
© The Author(s) 2020Resumen
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
Lista de ficheros
Google Scholar:Aguilar Saavedra, Juan Antonio
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Zaldívar Montero, Bryan
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