Combined Dark Matter searches towards dwarf spheroidal galaxies with Fermi-LAT, HAWC, HESS, MAGIC and VERITAS
Bibliographic record
Abstract
The search for Dark Matter (DM) has great potential to reveal physics beyond the Standard Model. As such, searches for evidence of DM particles are being carried out using a wide range of techniques, such as direct searches for DM particles, searches for DM produced with colliders, and indirect searches for the Standard Model annihilation products of DM. Dwarf spheroidal galaxies (dSphs) are excellent targets for indirect Dark Matter searches due to their relatively high DM content and negligible expected astrophysical background. A collaboration was formed to maximise the sensitivity of DM searches towards dSphs by combining for the first time dSph data from three imaging air Cherenkov telescope (IACT) arrays: HESS, MAGIC, and VERITAS; the Fermi-LAT satellite, and the water Cherenkov detector HAWC. Due to the diverse nature of the instruments involved, each experiment will analyse their individual datasets from multiple targets and then the results will be combined at the likelihood level. For consistency of the likelihoods across the five experiments, a common approach is used to treat the astrophysical factor (J-Factor) for each target and an agreed set of annihilation channels are considered. We also agree on a com- mon statistical approach and treatment of instrumental systematic uncertainties. The results are presented in terms of constraints on the velocity-weighted cross section for DM self-annihilation as a function of the DM particle mass.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".