Searching for exotic object companions in the dense core of NGC 362
Bibliographic record
Abstract
The dense cores of globular clusters (GCs) are efficient environments for the production of exotic stellar populations, including millisecond pulsars (MSPs), low-mass X-ray binaries (LMXBs), and cataclysmic variables (CVs). Most of these objects likely form through two- and three-body interactions and are useful tracers of the cluster’s dynamical evolution. In this work, we explore the exotic object population in the galactic GC NGC 362, searching for the optical counterpart of 33 X-ray sources identified within 1′ from the cluster centre. To this end, we exploited a large Hubble Space Telescope dataset obtained in eight different epochs and covering a wavelength range from the near UV to the optical I band. To identify the most promising counterparts, we followed a multi-step analysis based on four main ingredients, namely, positional coincidence, position in the colour–magnitude diagrams, Hα excess, and photometric variability. In addition, we complemented the photometric analysis with spectroscopic information coming from the analysis of MUSE radial velocity curves. Thanks to this multi-diagnostic approach, we were able to identify 28 high-confidence optical counterparts, including several candidate MSPs, active binaries, and CVs. The most intriguing counterparts include a candidate black widow system, an eclipsing binary blue straggler, and a system in outburst, potentially representing either an LMXB or a nova eruption from a CV. The candidate MSPs proposed in this work will contribute to ongoing radio analyses with MeerKAT for the identification and detailed study of MSPs in NGC 362.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".