Comparison of geometrical layouts for next-generation large-volume Cherenkov neutrino telescopes
Bibliographic record
Abstract
Abstract Water-(Ice-) Cherenkov neutrino telescopes have played a pivotal role in the search and discovery of high-energy astrophysical neutrinos. Experimental collaborations are developing and constructing next-generation neutrino telescopes with improved optical modules (OMs) and larger geometrical volumes to increase their efficiency in the multi-TeV energy range and extend their reach to EeV energies. Although most existing telescopes share similar OM layouts, more layout options should be explored for next-generation detectors to maximize discovery capability. In this work, we study a set of layouts at different geometrical volumes and evaluate the signal event selection efficiency and reconstruction fidelity under both an only trigger-level linear regression algorithm and an offline Graph Neural Network (GNN) reconstruction. Our methodology and findings serve as first steps toward an optimized, global network of neutrino telescopes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".