22 citations · 89 across the 28 of their papers we have counts for
3 papers · 1 filter
Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough
Gaia Grosso, Vinicius Mikuni, Lukas Heinrich
Machine learning (ML) has become integral to fundamental physics, accelerating statistical workflows from data acquisition through inference and hypothesis testing. As ML systems g…
Machine Learning-Assisted Unfolding for Neutrino Cross-section Measurements with the OmniFold Technique
Roger G. Huang, Andrew Cudd, Masaki Kawaue +4
The choice of unfolding method for a cross-section measurement is tightly coupled to the model dependence of the efficiency correction and the overall impact of cross-section model…
Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction
Etienne Dreyer, Eilam Gross, Dmitrii Kobylianskii +3
Detector simulation and reconstruction are a significant computational bottleneck in particle physics. We develop Particle-flow Neural Assisted Simulations (Parnassus) to address t…