Solar neutrino event spectra: Tuning SNO to equalize Super-Kamiokande
Bibliographic record
Abstract
The Super-Kamiokande (SK) and the Sudbury Neutrino Observatory (SNO) experiments are monitoring the flux of ${}^{8}\mathrm{B}$ solar neutrinos through the electron energy spectrum from the reactions ${\ensuremath{\nu}}_{e,\ensuremath{\mu},\ensuremath{\tau}}{+e}^{\ensuremath{-}}\ensuremath{\rightarrow}{\ensuremath{\nu}}_{e,\ensuremath{\mu},\ensuremath{\tau}}{+e}^{\ensuremath{-}}$ and ${\ensuremath{\nu}}_{e}+\stackrel{\ensuremath{\rightarrow}}{d}{p+p+e}^{\ensuremath{-}},$ respectively. We show that the SK detector response to ${}^{8}\mathrm{B}$ neutrinos in each bin of the electron energy spectrum (above 8 MeV) can be approximated, with good accuracy, by the SNO detector response in an appropriate electron energy range (above 5.1 MeV). For instance, the SK response in the bin $[10,10.5]$ MeV is reproduced (``equalized'') within $\ensuremath{\sim}2%$ by the SNO response in the range $[7.1,11.75]$ MeV. As a consequence, in the presence of active neutrino oscillations, the SK and SNO event rates in the corresponding energy ranges turn out to be linearly related, for any functional form of the oscillation probability. Such equalization is not spoiled by the possible contribution of hep neutrinos (within current phenomenological limits). In perspective, when the SK and the SNO spectra will both be measured with high accuracy, the SK-SNO equalization can be used to determine the absolute ${}^{8}\mathrm{B}$ neutrino flux, and to cross-check the (non)observation of spectral deviations in SK and SNO. At present, as an exercise, we use the equalization to ``predict'' the SNO energy spectrum, on the basis of the current SK data. Finally, we briefly discuss some modifications or limitations of our results in the case of sterile $\ensuremath{\nu}$ oscillations and of relatively large Earth matter effects.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".