Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Duarte, Javier M., Seljak, Uros, Terao, Kazu
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7115578052
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