Modern Machine Learning and Particle Physics Phenomenology at the LHC
Fuente:
arXiv
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911419703754752 |
|---|---|
| author | Ubiali, Maria |
| author_facet | Ubiali, Maria |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_03728 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Modern Machine Learning and Particle Physics Phenomenology at the LHC Ubiali, Maria High Energy Physics - Phenomenology 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. |
| title | Modern Machine Learning and Particle Physics Phenomenology at the LHC |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2602.03728 |