M31 globular cluster structures and the presence of X-ray binaries
Bibliographic record
Abstract
[Abridged] M31 has several times more globular clusters (GCs) than the Milky Way. It contains a correspondingly larger number of low mass X-ray binaries (LMXBs) associated with GCs, and can be used to investigate the GC properties which lead to X-ray binary formation. The best tracer of the spatial structure of M31 GCs is high-resolution imaging from the Hubble Space Telescope, and we have used HST data to derive structural parameters for 29 LMXB-hosting M31 GCs. These measurements are combined with structural parameters from the literature for a total of 41 (of 50 known) LMXB GCs and a comparison sample of 65 non-LMXB GCs. Structural parameters measured in blue bandpasses are found to show smaller core radii and higher concentrations than those measured in red bandpasses; this difference is enhanced in LMXB clusters and could be related to stellar population differences. Clusters with LMXBs show higher collision rates for their mass compared to those without LMXBs and collision rates estimated at the core radius show larger offsets than rates estimated at the half-light radius. These results are consistent with the dynamical formation scenario for LMXBs. A logistic regression analysis finds that, as expected, the probability of a GC hosting an LMXB increases with increasing collision rate and proximity to the galaxy center. The same analysis finds that P(LMXB) decreases with increasing GC mass at a fixed collision rate, although we caution that this could be due to sample selection effects. Metallicity is found to be a less important predictor of P(LMXB) than collision rate, mass, or distance, even though LMXB GCs have a higher metallicity on average. This may be due to the interaction of location and metallicity: a sample of M31 LMXBs with a greater range in galactocentric distance would likely contain more metal-poor GCs and make it possible to disentangle the two effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".