Producing a background free data set for measurement of the charge current flux and day-night asymmetry at the Sudbury Neutrino Observatory
Bibliographic record
Abstract
The SNO detector is a 1 kilo-tonne heavy water Cerenkov detector designed to solve the solar neutrino problem. The detector is situated 2km underground in the INCO Ltd. Creighton mine near Sudbury, Ontario. The heavy water is observed by approximately 9500 photo-multiplier tubes (PMTs) to detect Cerenkov light generated by solar neutrino interactions. Using heavy water SNO can detect neutrinos in three different ways. In this thesis aspects of the charge current (a reaction sensitive to electron neutrinos only) and elastic scattering flux analysis are presented. Some models predict a difference in the detected neutrino rate between day and night. Measurement of this via the day-night asymmetry can help solve the solar neutrino problem. One of the principal problems for the solar neutrino analysis are the instrumental backgrounds; events caused by processes other than Cerenkov light. This thesis contains the descriptions of the backgrounds, the data selection cuts that have been designed to remove them and the effect of these cuts on SNO data. To demonstrate the effectiveness of the cuts, the fraction of good events removed and the residual background content of the data set after application of the cuts are measured. Once the cuts have been applied to the data the charge current flux and day-night asymmetry can be measured. In this thesis data taken between the 2nd of November 1999 and the 1st July 2000, providing a total of 64.6 days of data during the day and 93.1 days of night data, are used to measure the day-night asymmetry. The results of this analysis for the asymmetry in the detected solar neutrino rate from the charge current and elastic scattering reactions are presented. (author)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".