44 citations · 65 across the 21 of their papers we have counts for
3 papers · 1 filter
Predict before you train: Scaling Laws for particle physics foundation models
Jan-Lucas Uslu, Benjamin Nachman, Christopher Re
The largest machine learning models in particle physics are also the most expensive to train, yet the return on scaling a given architecture cannot be estimated before that compute…
Cross-Domain Transfer with Particle Physics Foundation Models: From Jets to Neutrino Interactions
Gregor Krzmanc, Vinicius Mikuni, Benjamin Nachman +1
Future AI-based studies in particle physics will likely start from a foundation model to accelerate training and enhance sensitivity. As a step toward a general-purpose foundation…
Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision
Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457
Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape co…