High Energy Physic
High-energy physics research aims to understand fundamental particles and their interactions, often leveraging massive datasets from particle collider experiments. Current research heavily employs machine learning, focusing on developing and optimizing novel architectures like transformers, graph neural networks, and normalizing flows for tasks such as particle tracking, jet tagging, and event classification. These advancements improve the efficiency and accuracy of data analysis, enabling more precise measurements and potentially accelerating the discovery of new physics phenomena.
Papers
Fast Inference Using Automatic Differentiation and Neural Transport in Astroparticle Physics
Dorian W. P. Amaral, Shixiao Liang, Juehang Qin, Christopher Tunnell
Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
Jonas Spinner, Victor Bresó, Pim de Haan, Tilman Plehn, Jesse Thaler, Johann Brehmer
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
Lukas Heinrich, Tobias Golling, Michael Kagan, Samuel Klein, Matthew Leigh, Margarita Osadchy, John Andrew Raine
Finetuning Foundation Models for Joint Analysis Optimization
Matthias Vigl, Nicole Hartman, Lukas Heinrich