Search for Neutrinos from Annihilation of Captured Low-Mass Dark Matter Particles in the Sun by Super-Kamiokande
Bibliographic record
Abstract
Super-Kamiokande (SK) can search for weakly interacting massive particles (WIMPs) by detecting neutrinos produced from WIMP annihilations occurring inside the Sun. In this analysis, we include neutrino events with interaction vertices in the detector in addition to upward-going muons produced in the surrounding rock. Compared to the previous result, which used the upward-going muons only, the signal acceptances for light (few-GeV/c^{2}-200-GeV/c^{2}) WIMPs are significantly increased. We fit 3903 days of SK data to search for the contribution of neutrinos from WIMP annihilation in the Sun. We found no significant excess over expected atmospheric-neutrino background and the result is interpreted in terms of upper limits on WIMP-nucleon elastic scattering cross sections under different assumptions about the annihilation channel. We set the current best limits on the spin-dependent WIMP-proton cross section for WIMP masses below 200 GeV/c^{2} (at 10 GeV/c^{2}, 1.49×10^{-39} cm^{2} for χχ→bb[over ¯] and 1.31×10^{-40} cm^{2} for χχ→τ^{+}τ^{-} annihilation channels), also ruling out some fraction of WIMP candidates with spin-independent coupling in the few-GeV/c^{2} mass range.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".