The dark matter diffused supernova neutrino background
Bibliographic record
Abstract
Abstract We consider neutrinos scattering off Milky Way dark matter and the impact of this scattering on supernovae neutrinos. This can take the form of attenuation on the initial flux of neutrinos and a time-delayed flux of scattered neutrinos. Considering dark matter masses above 100 MeV and past Milky Way supernovae, we find this time-delayed flux is nearly constant in time. We call this flux the Dark Matter Diffused Supernova Neutrino Background (DMDSNB), and use Super-K limits on the Diffuse Supernova Neutrino Background (DSNB) flux to set limits on the dark matter-neutrino scattering cross section. We find σ DM-ν /m DM ≲ 2.4 × 10-24cm2/GeV for m DM ≳ 1 GeV, which is the strongest bound to date on dark matter-neutrino scatterings at MeV energies, and stronger than bounds set from SN1987A neutrino attenuation by an order of magnitude. We end by discussing how the DMDSNB could be distinguished from the DSNB.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".