3 papers
nucl-ex2025
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…
physics.data-an2025
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…
hep-ex2024
Unpaired Image Translation to Mitigate Domain Shift in Liquid Argon Time Projection Chamber Detector Responses
Yi Huang, Dmitrii Torbunov, Brett Viren +4
Deep learning algorithms often are trained and deployed on different datasets. Any systematic difference between the training and a test dataset may degrade the algorithm performan…