Prospects for dark matter observations in dwarf spheroidal galaxies with the Cherenkov Telescope Array Observatory
Bibliographic record
Abstract
ABSTRACT The dwarf spheroidal galaxies (dSphs) orbiting the Milky Way are widely regarded as systems supported by velocity dispersion against self-gravity, and as prime targets for the search for indirect dark matter (DM) signatures in the GeV-to-TeV $\gamma$-ray range owing to their lack of astrophysical $\gamma$-ray background. We present forecasts of the sensitivity of the forthcoming Cherenkov Telescope Array Observatory (CTAO) to annihilating or decaying DM signals in these targets. An original selection of candidates is performed from the current catalogue of known objects, including both classical and ultrafaint dSphs. For each, the expected DM content is derived using the most comprehensive photometric and spectroscopic data available, within a consistent framework of analysis. This approach enables the derivation of novel astrophysical factor profiles for indirect DM searches, which are compared with results from the literature. From an initial sample of 64 dSphs, eight promising targets are identified – Draco I, Coma Berenices, Ursa Major II, Ursa Minor, and Willman 1 in the North, Reticulum II, Sculptor, and Sagittarius II in the South – for which different DM density models yield consistent expectations, leading to robust predictions. CTAO is expected to provide the strongest limits above $\sim$10 TeV, reaching velocity-averaged annihilation cross sections of $\sim 5\times 10^{-25}$ cm$^3$ s$^{-1}$ and decay lifetimes up to $\sim 10^{26}$ s for combined limits. The dominant uncertainties arise from the imprecise determination of the DM content, particularly for ultrafaint dSphs. Observation strategies are proposed that optimize either deep exposures of the best candidates or diversified target selections.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".