STACKING STAR CLUSTERS IN M51: SEARCHING FOR FAINT X-RAY BINARIES
Bibliographic record
Abstract
The population of low-luminosity (<10 35 erg s −1 ) X-ray binaries (XRBs) has been investigated in our Galaxy and M31 but not further. To address this problem, we have used data from the Chandra X-Ray Observatory and the Hubble Space Telescope to investigate the faint population of XRBs in the grand-design spiral galaxy M51. A matching analysis found 25 star clusters coincident with 20 X-ray point sources within 1 5 (60 pc). From X-ray and optical color–color plots we determine that this population is dominated by high-mass XRBs. A stacking analysis of the X-ray data at the positions of optically identified star clusters was completed to probe low-luminosity X-ray sources. No cluster type had a significant detection in any X-ray energy band. An average globular cluster had the largest upper limit, 9.23 × 10 34 erg s −1 , in the full band (0.3–8 keV) while on average the complete sample of clusters had the lowest upper limit, 6.46 × 10 33 erg s −1 in the hard band (2–8 keV). We determined average luminosities of the young and old star cluster populations and compared the results to those from the Milky Way. We conclude that deeper X-ray data are required to identify faint sources with a stacking analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".