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arXiv · 1711.10840

Machine learning challenges in theoretical HEP

Stefano Carrazza
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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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Stefano Carrazza. 2017-11-29. Machine learning challenges in theoretical HEP. https://arxiv.org/abs/1711.10840

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