Liquid Argon Time Projection Chambers (LArTPCs) are a leading detector technology for precise 3D imaging and reconstruction of neutrino interactions, offering millimeter-scale spatial resolution. The SPINE (Scalable Particle Imaging with Neural Embeddings) reconstruction chain employs Sparse Convolutional Neural Networks for voxel-level feature extraction and Graph Neural Networks for...
Previously, we presented Panda (arXiv:2512.01324), a scalable point cloud architecture and training method that learns reusable sensor-level representations directly from raw, unlabeled 3D TPC data. We showed that this method–a UNet-like point cloud encoder coupled with a multi-view self-distillation objective–produces an encoder with foundation-model-like behavior: its self-taught...
The DUNE near detector will face high interaction rates that stress traditional optical reconstruction pipelines relying on heuristic metrics for interaction detection. To achieve a flexible, data-driven solution and narrow sim-to-real gaps, we explore self-supervised pretraining for optical waveform reconstruction in liquid argon time projection chambers. We pretrain a Conformer-based masked...
Recent advances in machine learning, particularly in multimodal models, have created new opportunities for analyzing complex data in high-energy physics, where accurate identification of particle interactions is critical for scientific discovery. However, existing approaches rely heavily on convolutional neural networks, which lack interpretability and do not fully leverage multimodal...
