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Bibliographic Details
Main Author: Hallin, Anna
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2509.21434
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author Hallin, Anna
author_facet Hallin, Anna
contents The rise of foundation models -- large, pretrained machine learning models that can be finetuned to a variety of tasks -- has revolutionized the fields of natural language processing and computer vision. In high-energy physics, the question of whether these models can be implemented directly in physics research, or even built from scratch, tailored for particle physics data, has generated an increasing amount of attention. This review, which is the first on the topic of foundation models in high-energy physics, summarizes and discusses the research that has been published in the field so far.
format Preprint
id arxiv_https___arxiv_org_abs_2509_21434
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Foundation models for high-energy physics
Hallin, Anna
High Energy Physics - Phenomenology
Artificial Intelligence
Machine Learning
High Energy Physics - Experiment
Data Analysis, Statistics and Probability
The rise of foundation models -- large, pretrained machine learning models that can be finetuned to a variety of tasks -- has revolutionized the fields of natural language processing and computer vision. In high-energy physics, the question of whether these models can be implemented directly in physics research, or even built from scratch, tailored for particle physics data, has generated an increasing amount of attention. This review, which is the first on the topic of foundation models in high-energy physics, summarizes and discusses the research that has been published in the field so far.
title Foundation models for high-energy physics
topic High Energy Physics - Phenomenology
Artificial Intelligence
Machine Learning
High Energy Physics - Experiment
Data Analysis, Statistics and Probability
url https://arxiv.org/abs/2509.21434