Diffuse supernova neutrino background search at Super-Kamiokande
Bibliographic record
Abstract
We have conducted a new search for the diffuse supernova neutrino background (DSNB) flux at Super-Kamiokande (SK), with a $22.5\ifmmode\times\else\texttimes\fi{}2970\text{\ensuremath{-}}\mathrm{kton}\ifmmode\cdot\else\textperiodcentered\fi{}\mathrm{day}$ exposure from its fourth operational phase IV. With the new analysis we improve on the existing background reduction techniques and systematic uncertainties and take advantage of an improved neutron tagging algorithm to lower the energy threshold compared to the previous phases of SK. This allows for setting the world's most stringent upper limit on the extraterrestrial ${\overline{\ensuremath{\nu}}}_{e}$ flux, for neutrino energies below 31.3 MeV. The SK-IV results are combined with the ones from the first three phases of SK to perform a joint analysis using $22.5\ifmmode\times\else\texttimes\fi{}5823\text{ }\text{ }\mathrm{kton}\ifmmode\cdot\else\textperiodcentered\fi{}\mathrm{days}$ of data. This analysis has the world's best sensitivity to the DSNB ${\overline{\ensuremath{\nu}}}_{e}$ flux, comparable to the predictions from various models. For neutrino energies larger than 17.3 MeV, the new combined 90% CL upper limits on the DSNB ${\overline{\ensuremath{\nu}}}_{e}$ flux lie around $2.7\text{ }\text{ }{\mathrm{cm}}^{\ensuremath{-}2}\ifmmode\cdot\else\textperiodcentered\fi{}{\mathrm{sec}}^{\ensuremath{-}1}$, strongly disfavoring the most optimistic predictions. Finally, potentialities of the gadolinium phase of SK and the future Hyper-Kamiokande experiment are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".