DARK MATTER DECAY AND ANNIHILATION IN THE LOCAL UNIVERSE: CLUES FROM <i>FERMI</i>
Bibliographic record
Abstract
We present all-sky simulated Fermi maps of γ-rays from dark matter (DM) decay and annihilation in the local universe. The DM distribution is obtained from a constrained cosmological simulation of the neighboring large-scale structure provided by the CLUES project. The DM fields of density and density squared are then taken as an input for the Fermi observation simulation tool to predict the γ-ray photon counts that Fermi would detect in 5 years of an all-sky survey for given DM models. Signal-to-noise ratio (S/N) sky maps have also been obtained by adopting the current Galactic and isotropic diffuse background models released by the Fermi Collaboration. We point out the possibility for Fermi to detect a DM γ-ray signal in local extragalactic structures. In particular, we conclude here that Fermi observations of nearby clusters (e.g., Virgo and Coma) and filaments are expected to give stronger constraints on decaying DM compared to previous studies. As an example, we find a significant S/N in DM models with a decay rate fitting the positron excess as measured by PAMELA. This is the first time that DM filaments are shown to be promising targets for indirect detection of DM. On the other hand, the prospects for detectability of annihilating DM in local extragalactic structures are less optimistic even with extreme cross-sections. We make the DM density and density squared maps publicly available online.
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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 | 0.001 |
| 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) | 0.002 | 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".