Machine learning challenges in theoretical HEP

TL;DR

This paper reviews the application of machine learning in theoretical high energy physics, focusing on classification of tasks, recent approaches, and the case study of parton distribution functions, highlighting future prospects.

Contribution

It provides a comprehensive classification of ML tasks in theoretical HEP and discusses recent approaches and future directions, especially in PDF determination.

Findings

ML techniques are increasingly applied in theoretical HEP.

Recent approaches show promising results in PDF determination.

Future developments will enhance ML integration in HEP-TH.

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.