11 citations · 31 across the 13 of their papers we have counts for
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physics.data-an2026
Track finding and particle identification performance with particle physics foundation models
Shuhang Li, Haiwang Yu, Gabor Galgoczi +8
Modern high energy nuclear and particle physics experiments produce large amounts of sparse data that must be reduced significantly in size before performing physics analysis. Rece…
physics.data-an2025
TPCpp-10M: Simulated proton-proton collisions in a Time Projection Chamber for AI Foundation Models
Shuhang Li, Yi Huang, David Park +10
Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field i…
physics.data-an2024★ 7 cited
Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments
Yeonju Go, Dmitrii Torbunov, Timothy Rinn +6
Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational auto-encoders, and normalizing flows, have been widely used and studied…