Charged-current and neutral-current event fraction determination based on fit vertices, time residuals and PMT hit angles for the Sudbury Neutrino Observatory
Bibliographic record
Abstract
The Sudbury Neutrino Observatory (SNO) aims to improve our understanding of neutrinos and energy-generating processes in the Sun. The central purpose of SNO is to compare the flux of charged-current (CC) and neutral-current (NC) events caused by solar neutrinos. The target in SNO will be 10⁶ kg of D₂O. One plan for enhancing the detection of NC events is by doping the D₂O with ³⁵Cl. This thesis describes maximum likelihood fitters created for the purpose of fitting CC events and for fitting NC events that result from neutron capture by a ³⁵Cl nucleus. For each fit event, likelihood ratios are extracted from these fitters to aid in determining the fraction of each type of event in a data set of unknown mixture. It is concluded that using both timing and angle information marginally improves the ability to discriminate between CC and NC events, compared with using angle information alone. This is seen in the reduced estimated error for a technique that determines the CC event fraction in a 50/50 mixed set of CC and NC events. For prototype sets with 2000 events each, and a mixed set with 950 of each event type, an event fraction determination based on only angle information produces a fraction estimate of 0.494 ± 0.037. With identically sized data sets, an event fraction determination that includes both timing and hit angle information produces and estimate of 0.493 ± 0.034.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".