EXPLORING X-RAY BINARY POPULATIONS in COMPACT GROUP GALAXIES with CHANDRA
Bibliographic record
Abstract
We obtain total galaxy X-ray luminosities, LX, originating from individually detected point sources in a sample of 47 galaxies in 15 compact groups of galaxies (CGs). For the great majority of our galaxies, we find that the detected point sources most likely are local to their associated galaxy, and are thus extragalactic X-ray binaries (XRBs) or nuclear active galactic nuclei (AGNs). For spiral and irregular galaxies, we find that, after accounting for AGNs and nuclear sources, most CG galaxies are either within the ±1σ scatter of the Mineo et al. LX-star formation rate (SFR) correlation or have higher LX than predicted by this correlation for their SFR. We discuss how these excesses may be due to low metallicities and high interaction levels. For elliptical and S0 galaxies, after accounting for AGNs and nuclear sources, most CG galaxies are consistent with the Boroson et al. LX-stellar mass correlation for low-mass XRBs, with larger scatter, likely due to residual effects such as AGN activity or hot gas. Assuming non-nuclear sources are low- or high-mass XRBs, we use appropriate XRB luminosity functions to estimate the probability that stochastic effects can lead to such extreme LX values. We find that, although stochastic effects do not in general appear to be important, for some galaxies there is a significant probability that high LX values can be observed due to strong XRB variability.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".