Strong optimized conservative Fermi-LAT constraints on dark matter models from the inclusive photon spectrum
Bibliographic record
Abstract
We set conservative, robust constraints on the annihilation and decay of dark matter into various Standard Model final states under various assumptions about the distribution of the dark matter in the Milky Way halo. We use the inclusive photon spectrum observed by the Fermi Gamma-ray Space Telescope through its main instrument, the Large Area Telescope. We use simulated data to first find the ``optimal'' regions of interest in the $\ensuremath{\gamma}$-ray sky, where the expected dark matter signal is largest compared with the expected astrophysical foregrounds. We then require the predicted dark matter signal to be less than the observed photon counts in the a priori optimal regions. This yields a very conservative constraint as we do not attempt to model or subtract astrophysical foregrounds. The resulting limits are competitive with other existing limits and, for some final states with cuspy dark-matter distributions in the Galactic Center region, disfavor the typical cross section required during freeze-out for a weakly interacting massive particle to obtain the observed relic abundance.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".