Cosmological constraints on dark matter annihilation and decay: Cross-correlation analysis of the extragalactic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>γ</mml:mi></mml:mrow></mml:math>-ray background and cosmic shear
Bibliographic record
Abstract
We derive constraints on dark matter (DM) annihilation cross section and decay lifetime from cross-correlation analyses of the data from Fermi-LAT and weak lensing surveys that cover a wide area of $\ensuremath{\sim}660$ squared degrees in total. We improve upon our previous analyses by using an updated extragalactic $\ensuremath{\gamma}$-ray background data reprocessed with the Fermi Pass 8 pipeline, and by using well-calibrated shape measurements of about twelve million galaxies in the Canada-France-Hawaii Lensing Survey (CFHTLenS) and Red-Cluster-Sequence Lensing Survey (RCSLenS). We generate a large set of full-sky mock catalogs from cosmological $N$-body simulations and use them to estimate statistical errors accurately. The measured cross-correlation is consistent with null detection, which is then used to place strong cosmological constraints on annihilating and decaying DM. For leptophilic DM, the constraints are improved by a factor of $\ensuremath{\sim}100$ in the mass range of $O(1)\text{ }\text{ }\mathrm{TeV}$ when including contributions from secondary $\ensuremath{\gamma}$ rays due to the inverse-Compton upscattering of background photons. Annihilation cross sections of $⟨\ensuremath{\sigma}v⟩\ensuremath{\sim}{10}^{\ensuremath{-}23}\text{ }\text{ }{\mathrm{cm}}^{3}/\mathrm{s}$ are excluded for TeV-scale DM depending on channel. Lifetimes of $\ensuremath{\sim}1{0}^{25}\text{ }\text{ }\mathrm{sec}$ are also excluded for the decaying TeV-scale DM. Finally, we apply this analysis to wino DM and exclude the wino mass around 200 GeV. These constraints will be further tightened, and all the interesting wino DM parameter region can be tested, by using data from future wide-field cosmology surveys.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".