Anna Stakia , Tommaso Dorigo , Giovanni Banelli , Daniela Bortoletto , Alessandro Casa , Pablo de Castro , Christophe Delaere , Julien Donini , Livio Finos , Michele Gallinaro , Andrea Giammanco , Alexander Held , Fabricio Jiménez Morales , Grzegorz Kotkowski , Seng Pei Liew , Fabio Maltoni , Giovanna Menardi , Ioanna Papavergou , Alessia Saggio , Bruno Scarpa , Andreas Weiler
{"title":"Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider","authors":"Anna Stakia , Tommaso Dorigo , Giovanni Banelli , Daniela Bortoletto , Alessandro Casa , Pablo de Castro , Christophe Delaere , Julien Donini , Livio Finos , Michele Gallinaro , Andrea Giammanco , Alexander Held , Fabricio Jiménez Morales , Grzegorz Kotkowski , Seng Pei Liew , Fabio Maltoni , Giovanna Menardi , Ioanna Papavergou , Alessia Saggio , Bruno Scarpa , Andreas Weiler","doi":"10.1016/j.revip.2021.100063","DOIUrl":null,"url":null,"abstract":"<div><p>Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named “AMVA4NewPhysics” studied the customization and application of advanced multivariate analysis methods and statistical learning tools to high-energy physics problems, as well as developed entirely new ones. Many of those methods were successfully used to improve the sensitivity of data analyses performed by the ATLAS and CMS experiments at the CERN Large Hadron Collider; several others, still in the testing phase, promise to further improve the precision of measurements of fundamental physics parameters and the reach of searches for new phenomena. In this paper, the most relevant new tools, among those studied and developed, are presented along with the evaluation of their performances.</p></div>","PeriodicalId":37875,"journal":{"name":"Reviews in Physics","volume":"7 ","pages":"Article 100063"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405428321000095/pdfft?md5=a17b8f3dedabe74d62b4b07fc2b9ffab&pid=1-s2.0-S2405428321000095-main.pdf","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reviews in Physics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405428321000095","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Physics and Astronomy","Score":null,"Total":0}
引用次数: 1
Abstract
Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named “AMVA4NewPhysics” studied the customization and application of advanced multivariate analysis methods and statistical learning tools to high-energy physics problems, as well as developed entirely new ones. Many of those methods were successfully used to improve the sensitivity of data analyses performed by the ATLAS and CMS experiments at the CERN Large Hadron Collider; several others, still in the testing phase, promise to further improve the precision of measurements of fundamental physics parameters and the reach of searches for new phenomena. In this paper, the most relevant new tools, among those studied and developed, are presented along with the evaluation of their performances.
期刊介绍:
Reviews in Physics is a gold open access Journal, publishing review papers on topics in all areas of (applied) physics. The journal provides a platform for researchers who wish to summarize a field of physics research and share this work as widely as possible. The published papers provide an overview of the main developments on a particular topic, with an emphasis on recent developments, and sketch an outlook on future developments. The journal focuses on short review papers (max 15 pages) and these are freely available after publication. All submitted manuscripts are fully peer-reviewed and after acceptance a publication fee is charged to cover all editorial, production, and archiving costs.