Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider
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 ne...
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oai:doaj.org-article:25b70fead16849348ede3777110406cd2021-11-20T05:07:47ZAdvances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider2405-428310.1016/j.revip.2021.100063https://doaj.org/article/25b70fead16849348ede3777110406cd2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405428321000095https://doaj.org/toc/2405-4283Between 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.Anna StakiaTommaso DorigoGiovanni BanelliDaniela BortolettoAlessandro CasaPablo de CastroChristophe DelaereJulien DoniniLivio FinosMichele GallinaroAndrea GiammancoAlexander HeldFabricio Jiménez MoralesGrzegorz KotkowskiSeng Pei LiewFabio MaltoniGiovanna MenardiIoanna PapavergouAlessia SaggioBruno ScarpaGiles C. StrongCecilia TosciriJoão VarelaPietro VischiaAndreas WeilerElsevierarticleParticle physicsCERN LHCCMSATLASAMVA4NewPhysicsMachine learningPhysicsQC1-999ENReviews in Physics, Vol 7, Iss , Pp 100063- (2021) |
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DOAJ |
| collection |
DOAJ |
| language |
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| topic |
Particle physics CERN LHC CMS ATLAS AMVA4NewPhysics Machine learning Physics QC1-999 |
| spellingShingle |
Particle physics CERN LHC CMS ATLAS AMVA4NewPhysics Machine learning Physics QC1-999 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 Giles C. Strong Cecilia Tosciri João Varela Pietro Vischia Andreas Weiler Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider |
| description |
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. |
| format |
article |
| author |
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 Giles C. Strong Cecilia Tosciri João Varela Pietro Vischia Andreas Weiler |
| author_facet |
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 Giles C. Strong Cecilia Tosciri João Varela Pietro Vischia Andreas Weiler |
| author_sort |
Anna Stakia |
| title |
Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider |
| title_short |
Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider |
| title_full |
Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider |
| title_fullStr |
Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider |
| title_full_unstemmed |
Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider |
| title_sort |
advances in multi-variate analysis methods for new physics searches at the large hadron collider |
| publisher |
Elsevier |
| publishDate |
2021 |
| url |
https://doaj.org/article/25b70fead16849348ede3777110406cd |
| work_keys_str_mv |
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1718419530975281152 |