A search for time dependence in astrophysical neutrino sources with the Sudbury Neutrino Observatory
Bibliographic record
Abstract
In this thesis I present the results of a periodicity study on the neutrino data collected over the span of the first two phases of SNO, at both low (1 day⁻¹ - 0.1 yr⁻¹) and high (1 day⁻¹ - 0.1 min⁻¹) frequency ranges. The high frequency study is the first of its kind, and is of particular interest in that it opens a window into the detection of solar g-mode oscillations, which have never been conclusively experimentally verified. In a data set with 7,646 neutrino candidates over a period of 698.29 live days, there was no detected high-frequency periodic signal. In addition to a wide-range, single-peak high-frequency search, I have performed a directed-region frequency analysis, and a noise-motivated broad-band analysis. All searches indicate an absence of periodicity in the 8B solar neutrino signal as measured by SNO. I have also carried out an analysis of time dependence in the context of a trigger-less burst search, with the motivation of either observing neutrinos from an optically occluded supernova, or setting an upper limit on the senstitivity of our detector for such an observation. I include discussions of backgrounds to such a search that are specific to a heavy-water Cherenkov detector such as SNO.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".