CURTAIN: Towards a Foundation Model for Neutrino Telescopes

By Magel, Yu, ArgüellesOct 22, 2026

Score████░░░░░░4.4

Key numbers

45% and its median energy
31% compared to training from
36% compared to training from

VerdictWorth a reader's time today.

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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.

Severin Magel, Felix J. Yu, Carlos A. Argüelles

The editor's rubric

Heuristic review

Leverage3 / 5: A method or resource many groups across the field will adopt within a year.; 18% of this desk’s rubric
Magnitude3 / 5: Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.; 20% of this desk’s rubric
Evidence3 / 5: Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.; 22% of this desk’s rubric
Novelty3 / 5: A genuinely new approach to an open problem.; 22% of this desk’s rubric
Trajectory2 / 5: Some room to improve with obvious engineering.; 10% of this desk’s rubric
Stakes2 / 5: Benefits a professional community (practitioners, clinicians, engineers).; 8% of this desk’s rubric

Dimension	Level	Weight	What that level means
Leverage	███░░ 3	18%	A method or resource many groups across the field will adopt within a year.
Magnitude	███░░ 3	20%	Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
Evidence	███░░ 3	22%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	███░░ 3	22%	A genuinely new approach to an open problem.
Trajectory	██░░░ 2	10%	Some room to improve with obvious engineering.
Stakes	██░░░ 2	8%	Benefits a professional community (practitioners, clinicians, engineers).

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose); gains (relative gain, outperforms); scale (scalable); stakes (energy).

How the score was computed

rank-2026-10-18

Score████░░░░░░4.4

Score = 10 × (75% × adjusted merit / 10 + 15% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit: 5.6 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.7 / 10; Shrunk toward the desk prior by editor confidence (40%).
Attention: 0%; Citations, upvotes, points, mentions.
Freshness: 91%; Half-life decay since publication.

No attention signals recorded yet.

The record

Reviewed by heuristic-v7 on Oct 23, 2026, 07:29 UTC. Paper type: method.
Categories: hep-ex, astro-ph.IM
TOP, No.6 in the Physics edition of October 23, 2026.