Machine-learning aided detector optimization of the Pacific Ocean Neutrino Experiment
Bibliographic record
Abstract
The Pacific Ocean Neutrino Experiment (P-ONE) is a planned cubic-kilometer-scale neutrino detector in the Pacific Ocean. P-ONE will measure high-energy astrophysical neutrinos to characterize the nature of astrophysical accelerators. Using existing deep-sea infrastructure provided by Ocean Networks Canada (ONC), P-ONE will instrument the ocean with optical modules - which host PMTs and readout electronics - deployed on several vertical cables of about 1 km in length. While the hardware design of the first prototype cable is currently being finalized, the detector geometry of the final instrument (up to 70 cables) is not yet fixed. Conventional design optimization typically requires extensive Monte-Carlo simulations, which limits the testable search space to a few configurations. In this contribution, we present the progress of optimizing the detector design using machine-learning-based surrogate models, which replace the computationally expensive MC simulations. By providing gradients, these models also allow for the efficient computation of detector resolutions via the Fisher Information Matrix, without having to rely on specific event-reconstruction algorithms. We present the physics sensitivities of various possible detector geometries calculated with the PLEnuM software tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".