The MAVERIC Survey: Radio Catalogs and Source Counts from Deep Very Large Array Imaging of 25 Galactic Globular Clusters
Bibliographic record
Abstract
Abstract The MAVERIC survey is the first deep radio continuum imaging survey of Milky Way globular clusters, with a central goal of finding and classifying accreting compact binaries, including stellar-mass black holes. Here we present radio source catalogs for 25 clusters with ultra-deep Karl G. Jansky Very Large Array observations. The median observing time was 10 hr per cluster, resulting in typical rms sensitivities of 2.3 and 2.1 μJy per beam at central frequencies of 5.0 and 7.2 GHz, respectively. We detect nearly 1300 sources in our survey at 5σ, and while many of these are likely to be background sources, we also find strong evidence for an excess of radio sources in some clusters. The radio spectral index distribution of sources in the cluster cores differs from the background, and shows a bimodal distribution. We tentatively classify the steep-spectrum sources (those much brighter at 5.0 GHz) as millisecond pulsars and the flat-spectrum sources as compact or other kinds of binaries. These provisional classifications will be solidified with the future addition of X-ray and optical data. The outer regions of our images represent a deep, relatively wide-field (∼0.4 deg2) and high-resolution C band background survey, and we present source counts calculated for this area. We also release radio continuum images for these 25 clusters to the community.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| 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) | 0.002 | 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".