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.
Highlights
Over the past several years machine learning (ML) has become one of the most popular and powerful sets of techniques and tools used for multidisciplinary scientific research
We focus the discussion on ML in theoretical High Energy Physics (HEPTH)
We take as a case study the recent development achieved for the determination of parton distribution functions (PDFs), where several techniques from ML are employed
Summary
Over the past several years machine learning (ML) has become one of the most popular and powerful sets of techniques and tools used for multidisciplinary scientific research. We take as a case study the recent development achieved for the determination of parton distribution functions (PDFs), where several techniques from ML are employed. Due to the specific nature of the problems addressed in HEP-TH these tools should be considered as part of ML applications because their development has contributed in exhaustive manner to the development of new techniques and methods in this field.
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