Development of Calibration Light Sources for the Pacific Ocean Neutrino Experiment
Bibliographic record
Abstract
The Pacific Ocean Neutrino Experiment (P-ONE) is a very-large-volume neutrino telescope proposed for deployment deep in the northern Pacific Ocean off the coast of British Columbia, Canada. Successful deployment of P-ONE will expand the observable skyline and increase the global detection rate of extraterrestrial neutrinos, expanding our understanding of their energetic sources across the cosmos. The detector will consist of an array of mooring lines instrumented with Precision Optical Modules (P-OMs) which detect Cherenkov light from secondary particles produced in neutrino interactions within the detector volume. For successfully reconstructing incident neutrinos, both the optical properties of seawater and the positions of each P-OM within the detector must be known to high precision. To achieve this goal, P-ONE will be designed to include a variety of calibration light sources for both localized and ranged measurements within the detector. These sources include unique P-ONE calibration modules (P-CALs) which are a combination of the detection elements of the P-OM with a well calibrated nanosecond flasher and small fast light flashers integrated into each P-OM for local calibration. This contribution highlights the current status of optical calibration light source development from initial simulations to results from lab tests of integrated flasher properties.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".