Indirect search for dark matter from the Galactic Center and halo with the Super-Kamiokande detector
Bibliographic record
Abstract
We present a search for an excess of neutrino interactions due to dark matter in the form of weakly interacting massive particles (WIMPs) annihilating in the Galactic center or halo based on the data set of Super-Kamiokande-I, -II, -III and -IV taken from 1996 to 2016. We model the neutrino flux, energy, and flavor distributions assuming WIMP self-annihilation is dominant to $\ensuremath{\nu}\overline{\ensuremath{\nu}}$, ${\ensuremath{\mu}}^{+}{\ensuremath{\mu}}^{\ensuremath{-}}$, $b\overline{b}$, or ${W}^{+}{W}^{\ensuremath{-}}$. The excess is in comparison to atmospheric neutrino interactions which are modeled in detail and fit to data. Limits on the self-annihilation cross section $⟨{\ensuremath{\sigma}}_{A}V⟩$ are derived for WIMP masses in the range 1 GeV to 10 TeV, reaching as low as $9.6\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}23}\text{ }\text{ }{\mathrm{cm}}^{3}\text{ }{\mathrm{s}}^{\ensuremath{-}1}$ for 5 GeV WIMPs in $b\overline{b}$ mode and $1.2\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}24}\text{ }\text{ }{\mathrm{cm}}^{3}\text{ }{\mathrm{s}}^{\ensuremath{-}1}$ for 1 GeV WIMPs in $\ensuremath{\nu}\overline{\ensuremath{\nu}}$ mode. The obtained sensitivity of the Super-Kamiokande detector to WIMP masses below several tens of GeV is the best among similar indirect searches to date.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".