papers

papers in reversed chronological order.

  1. Panda: Self-distillation of Reusable Sensor-level Representations for High Energy Physics
    tl;dr: An application of DINO-like self-distillation to unlabeled 3D images of particle interactions in a neutrino detector. We show the model learns to cleanly separate particles and interaction types in latent space, which reduced the number of labeled images required to reach the state-of-the-art for semantic segmentation by 1,000×.
    Samuel Young, and Kazuhiro Terao
    arXiv, 2025. Project, Code
  2. 10 Million Particle Events: Enabling Foundation Models for Sparse 3D Inverse Problems
    tl;dr: A proposal for a new dataset containing 10 million images of particle interactions with realistic detector response and multiple modalities.
    Omar Alterkait, Sam Young, Ka Vang Tsang, Junjie Xia, Carolyn H Smith, Taritree Wongjirad, and Kazuhiro Terao
    NeurIPS 2025 AI4Science Workshop (Spotlight), 2025
  3. Particle trajectory representation learning with masked point modeling
    tl;dr: We proposed a masked point modeling approach, PoLAr-MAE, for learning representations of particle trajectories from unlabeled LArTPC images.
    Samuel Young, Yeon-jae Jwa, and Kazuhiro Terao
    Machine Learning: Science and Technology, 2025. Project, Code
  4. The Optical Two- and Three-dimensional Fundamental Plane Correlations for Nearly 180 Gamma-Ray Burst Afterglows with Swift/UVOT, RATIR, and the Subaru Telescope
    M. G. Dainotti, S. Young, L. Li, D. Levine, K. K. Kalinowski, D. A. Kann, B. Tran, L. Zambrano-Tapia, A. Zambrano-Tapia, S. B. Cenko, M. Fuentes, E. G. Sánchez-Vázquez, S. R. Oates, N. Fraija, R. L. Becerra, A. M. Watson, N. R. Butler, J. J. Gonzalez, A. S. Kutyrev, W. H. Lee, J. X. Prochaska, E. Ramirez-Ruiz, M. G. Richer, and S. Zola
    The Astrophysical Journal Supplement Series, Jul 2022