How to Use Machine Learning to Improve the Discrimination between Signal and Background at Particle Colliders

The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, with the commercial and scientific fields being the most notorious ones. In particle physics, ML has been proven a useful resource to make the most of projects such as the Large Hadron Collider (LHC)....

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Autores principales:	Xabier Cid Vidal, Lorena Dieste Maroñas, Álvaro Dosil Suárez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	high-energy physics machine learning LHC LHCb Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/98c59779bc1448c9890aad3e86c9de61
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spelling	oai:doaj.org-article:98c59779bc1448c9890aad3e86c9de612021-11-25T16:43:46ZHow to Use Machine Learning to Improve the Discrimination between Signal and Background at Particle Colliders10.3390/app1122110762076-3417https://doaj.org/article/98c59779bc1448c9890aad3e86c9de612021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/11076https://doaj.org/toc/2076-3417The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, with the commercial and scientific fields being the most notorious ones. In particle physics, ML has been proven a useful resource to make the most of projects such as the Large Hadron Collider (LHC). The main advantage provided by ML is a reduction in the time and effort required for the measurements carried out by experiments, and improvements in the performance. With this work we aim to encourage scientists working with particle colliders to use ML and to try the different alternatives that are available, focusing on the separation of signal and background. We assess some of the most-used libraries in the field, such as Toolkit for Multivariate Data Analysis with ROOT, and also newer and more sophisticated options such as PyTorch and Keras. We also assess the suitability of some of the most common algorithms for signal-background discrimination, such as Boosted Decision Trees, and propose the use of others, namely Neural Networks. We compare the overall performance of different algorithms and libraries in simulated LHC data and produce some guidelines to help analysts deal with different situations. Examples include the use of low or high-level features from particle detectors or the amount of statistics that are available for training the algorithms. Our main conclusion is that the algorithms and libraries used more frequently at LHC collaborations might not always be those that provide the best results for the classification of signal candidates, and fully connected Neural Networks trained with Keras can improve the performance scores in most of the cases we formulate.Xabier Cid VidalLorena Dieste MaroñasÁlvaro Dosil SuárezMDPI AGarticlehigh-energy physicsmachine learningLHCLHCbTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 11076, p 11076 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	high-energy physics machine learning LHC LHCb Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	high-energy physics machine learning LHC LHCb Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Xabier Cid Vidal Lorena Dieste Maroñas Álvaro Dosil Suárez How to Use Machine Learning to Improve the Discrimination between Signal and Background at Particle Colliders
description	The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, with the commercial and scientific fields being the most notorious ones. In particle physics, ML has been proven a useful resource to make the most of projects such as the Large Hadron Collider (LHC). The main advantage provided by ML is a reduction in the time and effort required for the measurements carried out by experiments, and improvements in the performance. With this work we aim to encourage scientists working with particle colliders to use ML and to try the different alternatives that are available, focusing on the separation of signal and background. We assess some of the most-used libraries in the field, such as Toolkit for Multivariate Data Analysis with ROOT, and also newer and more sophisticated options such as PyTorch and Keras. We also assess the suitability of some of the most common algorithms for signal-background discrimination, such as Boosted Decision Trees, and propose the use of others, namely Neural Networks. We compare the overall performance of different algorithms and libraries in simulated LHC data and produce some guidelines to help analysts deal with different situations. Examples include the use of low or high-level features from particle detectors or the amount of statistics that are available for training the algorithms. Our main conclusion is that the algorithms and libraries used more frequently at LHC collaborations might not always be those that provide the best results for the classification of signal candidates, and fully connected Neural Networks trained with Keras can improve the performance scores in most of the cases we formulate.
format	article
author	Xabier Cid Vidal Lorena Dieste Maroñas Álvaro Dosil Suárez
author_facet	Xabier Cid Vidal Lorena Dieste Maroñas Álvaro Dosil Suárez
author_sort	Xabier Cid Vidal
title	How to Use Machine Learning to Improve the Discrimination between Signal and Background at Particle Colliders
title_short	How to Use Machine Learning to Improve the Discrimination between Signal and Background at Particle Colliders
title_full	How to Use Machine Learning to Improve the Discrimination between Signal and Background at Particle Colliders
title_fullStr	How to Use Machine Learning to Improve the Discrimination between Signal and Background at Particle Colliders
title_full_unstemmed	How to Use Machine Learning to Improve the Discrimination between Signal and Background at Particle Colliders
title_sort	how to use machine learning to improve the discrimination between signal and background at particle colliders
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/98c59779bc1448c9890aad3e86c9de61
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How to Use Machine Learning to Improve the Discrimination between Signal and Background at Particle Colliders

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