Machine and Deep Learning Applications in Particle Physics

TL;DR

This paper reviews how machine and deep learning techniques, especially boosted decision trees and neural networks, are revolutionizing data analysis and simulation in particle physics, fostering interdisciplinary growth and addressing big data challenges.

Contribution

It provides a comprehensive overview of recent applications and challenges of machine learning in particle physics, highlighting the bidirectional benefits for both fields.

Findings

Machine learning enhances data analysis in particle physics.

Deep learning models improve simulation accuracy.

Interdisciplinary collaboration advances both physics and AI.

Abstract

The many ways in which machine and deep learning are transforming the analysis and simulation of data in particle physics are reviewed. The main methods based on boosted decision trees and various types of neural networks are introduced, and cutting-edge applications in the experimental and theoretical/phenomenological domains are highlighted. After describing the challenges in the application of these novel analysis techniques, the review concludes by discussing the interactions between physics and machine learning as a two-way street enriching both disciplines and helping to meet the present and future challenges of data-intensive science at the energy and intensity frontiers.