A deep search for faint <i>Chandra</i> X-ray sources, radio sources, and optical counterparts in NGC 6752
Bibliographic record
Abstract
ABSTRACT We report the results of a deep search for faint Chandra X-ray sources, radio sources, and optical counterparts in the nearby, core-collapsed globular cluster, NGC 6752. We combined new and archival Chandra imaging to detect 51 X-ray sources (12 of which are new) within the 1.9 arcmin half-light radius. Three radio sources in deep ATCA 5 and 9 GHz radio images match with Chandra sources. We have searched for optical identifications for the expanded Chandra source list using deep Hubble Space Telescope photometry in B435, R625, H α, UV275, and U336. Among the entire sample of 51 Chandra sources, we identify 18 cataclysmic variables (CVs), 9 chromospherically active binaries (ABs), 3 red giants (RGs), 3 galaxies (GLXs), and 6 active galactic nuclei (AGNs). Three of the sources are associated with millisecond pulsars (MSPs). As in our previous study of NGC 6752, we find that the brightest CVs appear to be more centrally concentrated than the faintest CVs, although the effect is no longer statistically significant as a consequence of the inclusion in the faint group of two intermediate brightness CVs. This possible difference in the radial distributions of the bright and faint CV groups appears to indicate that mass segregation has separated them. We note that photometric incompleteness in the crowded central region of the cluster may also play a role. Both groups of CVs have an inferred mass above that of the main-sequence turnoff stars. We discuss the implications for the masses of the CV components.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".