3 papers
hep-ex2025
Panda: Self-distillation of Reusable Sensor-level Representations for High Energy Physics
Samuel Young, Kazuhiro Terao
Liquid argon time projection chambers (LArTPCs) provide dense, high-fidelity 3D measurements of particle interactions and underpin current and future neutrino and rare-event experi…
hep-ex2025
Particle Trajectory Representation Learning with Masked Point Modeling
Sam Young, Yeon-jae Jwa, Kazuhiro Terao
Effective self-supervised learning (SSL) techniques have been key to unlocking large datasets for representation learning. While many promising methods have been developed using on…
physics.data-an2024
Uncertainty Propagation within Chained Models for Machine Learning Reconstruction of Neutrino-LAr Interactions
Daniel Douglas, Aashwin Mishra, Daniel Ratner +2
Sequential or chained models are increasingly prevalent in machine learning for scientific applications, due to their flexibility and ease of development. Chained models are partic…