In these proceedings we perform a brief review of machine learning (ML) applications in theoretical High Energy Physics (HEP-TH). We start the discussion by defining and then classifying machine learning tasks in theoretical HEP. We then discuss some of the most popular and recent published approaches with focus on a relevant case study topic: the determination of parton distribution functions (PDFs) and related tools. Finally, we provide an outlook about future applications and developments due to the synergy between ML and HEP-TH.

Machine learning challenges in theoretical HEP / S. Carrazza. - In: JOURNAL OF PHYSICS. CONFERENCE SERIES. - ISSN 1742-6588. - 1085:2(2018), pp. 022003.1-022003.7. ((Intervento presentato al 18. convegno International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2017 tenutosi a Seattle nel 2017.

Machine learning challenges in theoretical HEP

Stefano Carrazza
2018

Abstract

In these proceedings we perform a brief review of machine learning (ML) applications in theoretical High Energy Physics (HEP-TH). We start the discussion by defining and then classifying machine learning tasks in theoretical HEP. We then discuss some of the most popular and recent published approaches with focus on a relevant case study topic: the determination of parton distribution functions (PDFs) and related tools. Finally, we provide an outlook about future applications and developments due to the synergy between ML and HEP-TH.
Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici
JOURNAL OF PHYSICS. CONFERENCE SERIES
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/615429
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