The MAVERIC Survey: Dynamical Origin of Radio Sources in Galactic Globular Clusters
Bibliographic record
Abstract
Abstract We investigate potential correlations between radio source counts (after background corrections) of 22 Galactic globular clusters (GCs) from the MAVERIC survey and the stellar encounter rates (Γ) and masses (M) of the GCs. Applying a radio luminosity limit of <?CDATA ${L}_{\mathrm{lim}}=5.0\times {10}^{27}\,\mathrm{erg}\,{{\rm{s}}}^{-1}$?> , we take a census of radio sources in the core and those within the half-light radius of each cluster. By following a maximum likelihood method and adopting a simplified linear model, we find an unambiguous dependence of core radio source counts on Γ and/or M at 90% confidence, but no clear dependence of source counts within the half-light radius on either Γ or M. Five of the identified radio sources in GC cores above our adopted limit are millisecond pulsars or neutron star X-ray binaries, the dependence of which on Γ is well known, but another is a published black hole (BH) X-ray binary candidate, and 10 others are not identified. Accounting for these verified cluster members increases the significance of the correlation with M and/or Γ (to 99% confidence) for fits to core and half-light region source counts, while excluding a dependence on Γ alone at 90% (core) and 68% (half-light) confidence. This is consistent with published dynamical simulations of GC BH interactions that argue Γ will be a poor predictor of the distribution of accreting BHs in GCs. Future multiwavelength follow-up to verify cluster membership will enable stronger constraints on the dependence of radio source classes on cluster properties, promising a new view on the dynamics of BHs in GCs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".