SciPost Submission Page
Modern Machine Learning and Particle Physics Phenomenology at the LHC
by Maria Ubiali
Submission summary
| Authors (as registered SciPost users): | Maria Ubiali |
| Submission information | |
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| Preprint Link: | scipost_202510_00040v1 (pdf) |
| Date submitted: | Oct. 22, 2025, 8:32 p.m. |
| Submitted by: | Maria Ubiali |
| 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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| Approach: | Phenomenological |
Abstract
Modern machine learning is driving a paradigm shift in particle physics phenomenology at the Large Hadron Collider. This short review examines the transformative role of machine learning across the entire theoretical prediction pipeline, from parton-level calculations to full simulations. We discuss applications to scattering amplitude computations, phase space integration, Parton Distribution Function determination, and parameter extraction. Some critical frontiers for the field including uncertainty quantification, the role of symmetries, and interpretability are highlighted.
Current status:
In refereeing