Searching for dark matter annihilation from individual halos: uncertainties, scatter and signal-to-noise ratios
Bibliographic record
Abstract
Individual extragalactic dark matter halos, such as those associated with nearby galaxies and galaxy clusters, are promising targets for searches for gamma rays from dark matter annihilation. We review the predictions for the annihilation flux from individual halos, focusing on the effect of current uncertainties in the concentration-mass relation and the contribution from halo substructure, and also estimating the intrinsic halo-to-halo scatter expected. After careful consideration of recent simulation results, we conclude that the concentrations of the smallest halos, while well-determined at high redshift, are still uncertain by a factor of 4–6 when extrapolated to low redshift. This in turn produces up to two orders of magnitude uncertainty in the predicted annihilation flux for any halo mass above this scale. Substructure evolution, the small-scale cutoff to the power spectrum, cosmology, and baryonic effects all introduce smaller, though cumulative, uncertainties. We then consider intrinsic variations from halo to halo. These arise from variations in concentration and substructure, leading to a scatter of ∼2.5 in the predicted annihilation luminosity. Finally, we consider the problem of detecting gamma rays from annihilation, given the expected contributions from other sources. We estimate the signal-to-noise ratio for gamma-ray detection as a function of halo mass, assuming that the isotropic gamma-ray background and cosmic rays from star formation are the main noise sources in the detection. This calculation suggests that group-scale halos, individually or in stacks, may be a particularly interesting target for the next generation of annihilation searches.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".