Speaker
Description
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 autoencoder on synthetic single-PMT optical waveforms and finetune for interaction time detection, evaluating against supervised baselines in flash detection accuracy and photon count regression. We further extend this framework to multi-PMT waveforms with signal rates comparable to a challenging environment such as the DUNE near detector LArTPC, where the model learns structured spatiotemporal representations. We present quantitative results assessing whether masked autoencoding can serve as a scalable, label-efficient pretraining strategy for optical reconstruction in next-generation LArTPC detectors.
| Contribution types | Short talk (15min + 5min Q/A) |
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