1 citations · 1 across the 4 of their papers we have counts for
5 papers
How to scale your HEP ML models: A recipe for robust architecture comparisons at scale
Matthias Vigl, Nikita Pond, Jackson Barr +5
Much of the recent progress in machine learning domains such as language models has come from scaling laws that predict performance as a function of training effort. In high-energy…
Exploring the Boundaries of Differentiable Radiation Transport and Detector Simulation
Jeffrey Krupa, Yiyang Zhao, Mihaly Novak +9
We present an application of automatic differentiation for particle transport through matter using a Geant4-like radiation transport simulation with a full electromagnetic physics…
On the Codesign of Scientific Experiments and Industrial Systems
Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas +84
The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for in…
Neural Scaling Laws for Boosted Jet Tagging
Matthias Vigl, Nicole Hartman, Michael Kagan +1
The success of Large Language Models (LLMs) has established that scaling compute, through joint increases in model capacity and dataset size, is the primary driver of performance i…
Machine Learning for the Cluster Reconstruction in the CALIFA Calorimeter at R3B
Tobias Jenegger, Nicole Hartman, Roman Gernhaeuser +2
The R3B experiment at FAIR studies nuclear reactions using high-energy radioactive beams. One key detector in R3B is the CALIFA calorimeter consisting of 2544 CsI(Tl) scintillator…