7 papers
Evaluating Application Characteristics for GPU Portability Layer Selection
Mohammad Atif, Meghna Bhattacharya, Mark Dewing +12
GPUs have become the dominant source of computing power for high performance computing and are increasingly being used across the High Energy Physics computing landscape for a wide…
DNN-based Signal Processing for Liquid Argon Time Projection Chambers
Avinay Bhat, Mun Jung Jung, Gray Putnam +1
We investigate a deep learning-based signal processing for liquid argon time projection chambers (LArTPCs), a leading detector technology in neutrino physics. Identifying regions o…
Robust and Generalizable Background Subtraction on Images of Calorimeter Jets using Unsupervised Generative Learning
Yeonju Go, Dmitrii Torbunov, Yi Huang +8
Accurate separation of signal from background is one of the main challenges for precision measurements across high-energy and nuclear physics. Conventional supervised learning meth…
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…
FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics
David Park, Shuhang Li, Yi Huang +9
Large language models have revolutionized artificial intelligence by enabling large, generalizable models trained through self-supervision. This paradigm has inspired the developme…
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…