The 8LI calibration source and through-going muon analysis in the Sudbury Neutrino Observatory
Bibliographic record
Abstract
This thesis deals with two topics of relevance to the Sudbury Neutrino Observatory (SNO). The first part describes the concept, design, testing, deployment, and preliminary data analysis of a 8Li source deployed in the SNO detector. This source is a short-lifetime beta-emitting radioactive source is used to measure the detector position resolution, and the efficiency (i.e. sacrifice) of solar neutrino analysis data reduction schemes. The utility of this source to perform energy calibrations assisting in measurement of 8B neutrino energy spectral distortions is also discussed. The second part is an analysis of through-going muons in SNO. An algorithm for reconstruction muon tracks was implemented and tested using a Monte Carlo program, then applied to 149 days of SNO data. The distribution of muon directions was analyzed in two contexts: down-going muons from atmospheric cosmic rays, and atmospheric neutrino-induced muons. Atmospheric muons are analyzed to obtain a value for the power index of the primary all-nucleon spectrum of [gamma] = 2.80 ± 0.04(' stat') ± 0.08('sys'). A limit on prompt muon flux cannot be made without assumptions, but a limit of ' Rc' < 5.0 * 10-3 can be set if [gamma] is constrained to be less than 2.9. Neutrino-induced muon data is given a preliminary analysis which is inconclusive due to limited statistics, but indicative that SNO will be able to produce a model-independent measurement of the absolute high-energy neutrino flux by looking at the component of neutrino-induced muons coming from zenith angles above the horizon. Early data exclude oscillations with [Delta]'m' 2 > 0.1eV2 at sin22[theta]' V' = 1.
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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 | 0.001 | 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) | 0.001 | 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".