Foundation models for high-energy physics
It provides a foundational overview for researchers in high-energy physics, but is incremental as it reviews existing work without new results.
This review addresses the potential of foundation models in high-energy physics by summarizing existing research on their implementation and customization for particle physics data.
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.