Machine Learning

TL;DR

This chapter provides an overview of machine learning concepts, emphasizing algorithms that learn from data and their applications across various particle physics domains such as energy, cosmic, and accelerator research.

Contribution

It introduces core machine learning concepts and illustrates their relevance and applications in particle physics research.

Findings

ML algorithms effectively identify patterns in particle physics data

Applications improve data analysis and decision-making in physics experiments

Examples span energy, cosmic, and accelerator physics domains

Abstract

This chapter gives an overview of the core concepts of machine learning (ML) -- the use of algorithms that learn from data, identify patterns, and make predictions or decisions without being explicitly programmed -- that are relevant to particle physics with some examples of applications to the energy, intensity, cosmic, and accelerator frontiers.