Searching for the MSW Effect and Other New Physics With the Boron-8 Solar Neutrino Spectrum (Final Technical Report)
Bibliographic record
Abstract
Over the past year, our group here at the University of Texas has continued to focus primarily on analysis of data from the Sudbury Neutrino Observatory (SNO), in particular on a push to lower the energy threshold used in solar neutrino analyses and on a search for short time-scale astrophysical phenomena in the neutrino data set. We have in addition begun R and D and simulation work on a new direct search for dark matter as part of the DEAP/CLEAN collaboration, as well as an effort associated with work on a new experiment using the existing SNO detector (SNO+). Lastly, we have also been doing some very early studies of an experiment to measure the neutrino mass using the beta decay of cold tritium atoms. Our work on SNO has focused primarily on making a low-threshold spectral measurement of the flux of {sup 8}B solar neutrinos. The work forms the bulk of graduate student Stan Seibert's PhD thesis. Nearly all systematic uncertainties associated with the analysis have now been measured, in particular the dominant uncertainties such as energy scale and resolution, and the uncertainty on SNO's 'isotropy' parameter used to distinguish electrons, neutrons, and radioactive backgrounds. It is now clear that we will be able to fit the {sup 8}B energy spectrum to a threshold of 4 MeV or below, with uncertainties in the 4 MeV bin somewhere between 15-20%. This will be the lowest threshold measurement ever made using the water Cherenkov technique, and will provide a test of the MSW-predicted distortion of the {sup 8}B energy spectrum. As an additional benefit of the low threshold analysis, we expect to get total uncertainties on the NC flux in the neighborhood of 4%, nearly a factor of 2 better than any of SNO's previous measurements of the total {sup 8}B flux.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".