Panda: Self-distillation of Reusable Sensor-level Representations for High Energy Physics

Panda teaser figure showing the pipeline from raw LArTPC data through self-supervised learning to downstream tasks

Panda overview. Raw charge depositions corresponding to particle trajectories recorded by a time projection chamber (TPC) (top left) are passed through a point-native hierarchical encoder pre-trained via self-distillation to produce a shared embedding (top right). The same pretrained features are used for three downstream tasks with lightweight heads (bottom): semantic segmentation; particle-level clustering; and interaction-level clustering that groups causally related particles.

Abstract

Liquid argon time projection chambers (LArTPCs) provide dense, high-fidelity 3D measurements of particle interactions and underpin current and future neutrino and rare-event experiments. Physics reconstruction typically relies on complex detector-specific pipelines that use tens of hand-engineered pattern recognition algorithms or cascades of task-specific neural networks that require extensive, labeled simulation.

We introduce Panda, a model that learns reusable sensor-level representations directly from raw unlabeled LArTPC data. Panda couples a hierarchical sparse 3D encoder with a multi-view, prototype-based self-distillation objective. On a simulated dataset, Panda substantially improves label efficiency and reconstruction quality, beating the previous state-of-the-art semantic segmentation model with 1,000× fewer labels. We also show that a single set-prediction head 1/20th the size of the backbone with no physical priors trained on frozen outputs from Panda can result in particle identification that is comparable with state-of-the-art reconstruction tools.

Method

Self-distillation architecture diagram showing student and teacher paths with EMA updates — Self-distillation training scheme

Backbone

We use a shared 90M-parameter point-native hierarchical encoder that operates directly on voxelized 3D charge clouds from LArTPC images. The same encoder is used for self-distilled pre-training and all downstream tasks.

Self-Distillation

We employ a prototype-based self-distillation scheme inspired by DINO and Sonata. A student network learns to predict consistent prototype distributions across strong augmentations by matching the outputs of an exponential moving average (EMA) teacher network.

Dimensionality reduction visualization of learned embeddings showing particle type clustering — t-SNE visualization of per-point embeddings taken from the output of the backbone from 1,000 images.

Learned Representations

The embeddings, cast to 2D using t-SNE, capture both inter-class diversity and intra-class multi-modality. For example, electrons manifest as showers, Michel electrons, or Delta rays, and the model learns to separate these naturally.

Some overlap between γ/e and μ/π clusters reflects genuine physical ambiguities in LArTPC data. For example, photon- and electron-initiated electromagnetic showers can be indistinguishable should there be no resolvable conversion gap and unreliable energy deposition (dE/dx) patterns.

Citation

@misc{young2025pandaselfdistillationreusablesensorlevel,
    title={Panda: Self-distillation of Reusable Sensor-level Representations for High Energy Physics}, 
    author={Samuel Young and Kazuhiro Terao},
    year={2025},
    eprint={2512.01324},
    archivePrefix={arXiv},
    primaryClass={hep-ex},
    url={https://arxiv.org/abs/2512.01324}, 
}

Panda
Self-distillation of Reusable Sensor-level Representations for High Energy Physics