Multi-messenger high-energy signatures of decaying dark matter and the effect of background light
Bibliographic record
Abstract
Abstract The IceCube Neutrino Observatory at the South Pole has measured astrophysical neutrinos using through-going and starting events in the TeV to PeV energy range. The origin of these astrophysical neutrinos is still largely unresolved, and among their potential sources could be dark matter decay. Measurements of the astrophysical flux using muon neutrinos are in slight tension with starting event measurements. This tension is driven by an excess observed in the energy range of 40–200 TeV with respect to the through-going expectation. Previous works have considered the possibility that this excess may be due to heavy dark matter decay and have placed constraints using gamma-ray and neutrino data. However, these constraints are not without caveats, since they rely on the modeling of the astrophysical neutrino flux and the sources of gamma-ray emission. In this work, we derive background-agnostic galactic and extragalactic constraints on decaying dark matter by considering Tibet-ASγ data, Fermi-LAT diffuse data, and the IceCube high-energy starting event sample. For the gamma-ray limits, we investigate the uncertainties on secondary emission from electromagnetic cascades during propagation arising from the unknown intensity of the extragalactic background light. We find that such uncertainties amount to a variation of up to ∼ 55% in the gamma-ray limits derived with extragalactic data. Our results imply that a significant fraction of the astrophysical neutrino flux could be due to dark matter and that ruling it out depends on the assumptions on the gamma-ray and neutrino background. The latter depends on the yet unidentified sources.
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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 | 0.002 |
| 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".