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.
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1711.10840.pdf
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Carrazza_2018_J._Phys.__Conf._Ser._1085_022003.pdf
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