A search for supernova neutrinos with the Sudbury Neutrino Observatory
Bibliographic record
Abstract
The Sudbury Neutrino Observatory (SNO) is an underground Cerenkov detector designed to detect neutrinos from astrophysical sources. The fiducial mass of the detector consists of 1000 tonnes of D₂0 , which provides sensitivity to all neutrino flavours. Since much of the energy released in the supernova burst is expected to be carried by the muon and tau neutrinos, the supernova signal recorded by the SNO detector is of particular importance. In addition, SNO is also sensitive to the prompt electron neutrino signal expected from capture processes during core collapse. Various supernova models are investigated and predictions of the SNO supernova signal are studied using simulated Monte Carlo data. A data analysis program to identify neutrinos from a galactic supernova burst has been installed in the online system at SNO. The program automatically analyzes burst data and it is anticipated that a manual alert to the Supernova Early Warning System could be issued within 20-30 minutes with negligible possibility of a false alarm. The burst identification algorithm currently in use both online and offline provides detection sensitivity beyond the far edge of our galaxy. A search for supernova neutrinos was performed using 241.0 days of data collected over the time period between November 2, 1999 and January 4, 2001. No candidate bursts were observed over this period, which places a 90% confidence level upper limit of < 3.5 galactic supernovae per year.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".