VERITAS dark matter search in dwarf Spheroidal galaxies: an extended source analysis
Bibliographic record
Abstract
Dark matter (DM) is largely believed to be the dominant component of the matter content of the Universe. Astronomical measurements can be utilized to search for Standard Model (SM) annihilation or decay products of DM, complementing direct and collider-based searches. Among DM particle candidates, Weakly Interacting Massive Particles (WIMPs) are an attractive one. Their decay or annihilation could produce secondary particles including very-high-energy (VHE: $E>100$ GeV) gamma rays, which could be detected by imaging atmospheric Cherenkov telescopes (IACTs). One of the most favourable target classes for DM searches are dwarf spheroidal galaxies (dSphs), dark matter-dominated objects with a negligible predicted gamma-ray emission due to apparent absence of gas and on-going star formation. IACTs, whose point spread functions (PSFs, defined as 68% containment radius) are typically $0.1^{\circ}$ at 1 TeV, have the necessary angular resolution to detect extended emission from some dSphs. Thus, an extended-source analysis may give an improvement to DM sensitivity, compared to a point-source analysis. In this work, we used observations made since 2007 to 2013 by VERITAS, an array of four imaging atmospheric Cherenkov telescopes sensitive to VHE gamma rays in the 100 GeV - 30 TeV energy range. We performed an unbinned maximum likelihood estimation incorporating the dSph angular profiles of four dSphs and tested its effectiveness against the traditional spectral analysis.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 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".