CURTAIN: Towards a Foundation Model for Neutrino Telescopes
Abstract
Neutrino telescopes record more than a trillion photon detections per year, yet there does not exist a self-supervised learning (SSL) task which fully satisfies all three conditions: improving as the pretraining set grows, transferring to a different detector, and outperforming the best model trained without pretraining at all. We introduce CURTAIN, a temporal masking scheme and the first SSL task for neutrino telescopes that achieves all of these goals. Downstream performance monotonically improves with the number of pretraining events, as predicted by scaling laws. Our encoder builds on DeepIce, currently the leading model on the NuBench benchmarks direction reconstruction task. Pretraining it with CURTAIN reduces its median angular error by 45% and its median energy error by 31% compared to training from scratch. It also transfers to a detector geometry never seen in pretraining, reducing the median angular error by 36% compared to training from scratch. At matched capacity and pretraining data, CURTAIN-pretrained encoders beat those pretrained by existing SSL tasks on both tasks and at every pretraining set size.
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BibTeX
@misc{magel2026curtain,
title = {{CURTAIN: Towards a Foundation Model for Neutrino Telescopes}},
author = {Severin Magel and Felix J. Yu and Carlos A. Argüelles},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.30894},
eprint = {2610.30894},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.30894},
}
RIS
TY - UNPB
TI - CURTAIN: Towards a Foundation Model for Neutrino Telescopes
AU - Severin Magel
AU - Felix J. Yu
AU - Carlos A. Argüelles
PY - 2026
DA - 2026/10/22
PB - arXiv
DO - 10.48550/arxiv.2610.30894
UR - https://arxiv.org/abs/2610.30894
AB - Neutrino telescopes record more than a trillion photon detections per year, yet there does not exist a self-supervised learning (SSL) task which fully satisfies all three conditions: improving as the pretraining set grows, transferring to a different detector, and outperforming the best model trained without pretraining at all. We introduce CURTAIN, a temporal masking scheme and the first SSL task for neutrino telescopes that achieves all of these goals. Downstream performance monotonically improves with the number of pretraining events, as predicted by scaling laws. Our encoder builds on DeepIce, currently the leading model on the NuBench benchmarks direction reconstruction task. Pretraining it with CURTAIN reduces its median angular error by 45% and its median energy error by 31% compared to training from scratch. It also transfers to a detector geometry never seen in pretraining, reducing the median angular error by 36% compared to training from scratch. At matched capacity and pretraining data, CURTAIN-pretrained encoders beat those pretrained by existing SSL tasks on both tasks and at every pretraining set size.
ER -