Characterization of dark-matter-induced anisotropies in the diffuse gamma-ray background
Bibliographic record
Abstract
The Fermi-LAT collaboration has recently reported the detection of angular power above the photon noise level in the diffuse gamma-ray background between 1 and 50 GeV. Such signal can be used to constrain a possible contribution from dark matter (DM) induced photons. We estimate the intensity and features of the angular power spectrum (APS) of this potential DM signal, for both decaying and annihilating DM candidates, by constructing template all-sky gamma-ray maps for the emission produced in the galactic halo and its substructures, as well as in extragalactic (sub)haloes. The DM distribution is given by state-of-the-art N-body simulations of cosmic structure formation, namely Millennium-II for extragalactic (sub)haloes, and Aquarius for the galactic halo and its subhaloes. We use a hybrid method of extrapolation to account for (sub)structures that are below the resolution limit of the simulations, allowing us to estimate the total emission all the way down to the minimal self-bound halo mass. We describe in detail the features appearing in the APS of our template maps and we estimate the effect of various uncertainties such as the value of the minimal halo mass, the fraction of substructures hosted in a halo and the shape of the DM density profile. Our results indicate that the fluctuation APS of the DM-induced emission is of the same order as the Fermi-LAT APS, suggesting that one can constrain this hypothetical emission from the comparison with the measured anisotropy. We also quantify the uncertainties affecting our results, finding ‘theoretical error bands’ spanning more than two orders of magnitude and dominated (for a given particle physics model) by our lack of knowledge of the abundance of low-mass (sub)haloes.
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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) | 0.000 | 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 | 0.000 |
| 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".