Solar neutrino measurements using the full data period of Super-Kamiokande-IV
Bibliographic record
Abstract
An analysis of solar neutrino data from the fourth phase of Super-Kamiokande (SK-IV) from October 2008 to May 2018 is performed and the results are presented. The observation time of the dataset of SK-IV corresponds to 2970 days and the total live time for all four phases is 5805 days. For more precise solar neutrino measurements, several improvements are applied in this analysis: lowering the data acquisition threshold in May 2015, further reduction of the spallation background using neutron clustering events, precise energy reconstruction considering the time variation of the PMT gain. The observed number of solar neutrino events in 3.49–19.49 MeV electron kinetic energy region during SK-IV is 65,443 − 388 + 390 ( stat . ) ± 925 ( syst . ) events. Corresponding B 8 solar neutrino flux is ( 2.314 ± 0.014 ( stat . ) ± 0.040 ( syst . ) ) × 10 6 cm − 2 s − 1 , assuming a pure electron-neutrino flavor component without neutrino oscillations. The flux combined with all SK phases up to SK-IV is ( 2.336 ± 0.011 ( stat . ) ± 0.043 ( syst . ) ) × 10 6 cm − 2 s − 1 . Based on the neutrino oscillation analysis from all solar experiments, including the SK 5805 days dataset, the best-fit neutrino oscillation parameters are sin 2 θ 12 , solar = 0.306 ± 0.013 and Δ m 21 , solar 2 = ( 6.1 0 − 0.81 + 0.95 ) × 10 − 5 eV 2 , with a deviation of about 1.5 σ from the Δ m 21 2 parameter obtained by KamLAND. The best-fit neutrino oscillation parameters obtained from all solar experiments and KamLAND are
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".