SciPost Submission Page
Foundation models for high-energy physics
by Anna Hallin
Submission summary
| Authors (as registered SciPost users): | Anna Hallin |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2509.21434v2 (pdf) |
| Date submitted: | Jan. 12, 2026, 10:05 a.m. |
| Submitted by: | Anna Hallin |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 2nd European AI for Fundamental Physics Conference (EuCAIFCon2025) |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Experimental, Computational, Phenomenological |
Abstract
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.
Current status:
Refereeing in preparation