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
March 27, 2024
March 18, 2024
February 27, 2024
February 19, 2024
February 18, 2024
February 1, 2024
January 24, 2024
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
January 16, 2024
December 8, 2023
November 30, 2023
November 29, 2023
November 24, 2023
November 21, 2023
November 6, 2023
September 25, 2023
September 19, 2023
September 12, 2023
August 31, 2023