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) [10.1088/1742-6596/1085/2/022003].
Machine learning challenges in theoretical HEP
S. 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.File | Dimensione | Formato | |
---|---|---|---|
1711.10840.pdf
accesso aperto
Tipologia:
Pre-print (manoscritto inviato all'editore)
Dimensione
345.89 kB
Formato
Adobe PDF
|
345.89 kB | Adobe PDF | Visualizza/Apri |
Carrazza_2018_J._Phys.__Conf._Ser._1085_022003.pdf
accesso aperto
Tipologia:
Publisher's version/PDF
Dimensione
399.86 kB
Formato
Adobe PDF
|
399.86 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.