Calibrating X-Ray Binary Luminosity Functions via Optical Reconnaissance. II. The High-mass XLF and Globular Cluster Population of X-Ray Binaries in the Low Star-forming Spiral M81
Bibliographic record
Abstract
Abstract We characterize the optical counterparts to the compact X-ray source population within the nearby spiral galaxy M81 using multiband Hubble Space Telescope (HST) imaging data. By comparing the optical luminosities and colors measured for candidate donor stars and host clusters to stellar and cluster evolutionary models, respectively, we estimate the likely masses and upper age limits of the field and cluster X-ray binaries. We identify 15 low-mass X-ray binaries (i.e., donor star mass ≲ 3 M ⊙) within ancient globular clusters, as well as 42 candidate high-mass X-ray binaries (i.e., donor star mass ≳ 8 M ⊙). To estimate the likelihood of misclassifications, we inject 4000 artificial sources into the HST mosaic image and conclude that our classifications of globular clusters and high-mass X-ray binaries are reliable at the >90% level. We find that globular clusters that host X-ray binaries are on average more massive and more compact than globular clusters that do not. However, there is no apparent correlation between the X-ray brightness of the clusters and their masses or densities, nor are X-ray binary hosts more X-ray luminous than the general field population of low-mass X-ray binaries. This work represents one of the first in-depth analyses of the population of X-ray binaries within globular clusters in a spiral galaxy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".