X-Ray Binary Luminosity Function Scaling Relations in Elliptical Galaxies: Evidence for Globular Cluster Seeding of Low-mass X-Ray Binaries in Galactic Fields
Bibliographic record
Abstract
Abstract We investigate X-ray binary (XRB) luminosity function (XLF) scaling relations for Chandra-detected populations of low-mass XRBs (LMXBs) within the footprints of 24 early-type galaxies. Our sample includes Chandra and Hubble Space Telescope observed galaxies at Mpc that have estimates of the globular cluster (GC) specific frequency ( S N ) reported in the literature. As such, we are able to directly classify X-ray-detected sources as being coincident with unrelated background/foreground objects, GCs, or sources that are within the fields of the galaxy targets. We model the GC and field LMXB population XLFs for all galaxies separately and then construct global models characterizing how the LMXB XLFs vary with galaxy stellar mass and S N . We find that our field LMXB XLF models require a component that scales with S N and has a shape consistent with that found for the GC LMXB XLF. We take this to indicate that GCs are “seeding” the galactic field LMXB population, through the ejection of GC LMXBs and/or the diffusion of the GCs in the galactic fields themselves. However, we also find that an important LMXB XLF component is required for all galaxies that scales with stellar mass, implying that a substantial population of LMXBs are formed “in situ,” which dominates the LMXB population emission for galaxies with S N ≲ 2. For the first time, we provide a framework quantifying how directly associated GC LMXBs, GC-seeded LMXBs, and in situ LMXBs contribute to LMXB XLFs in the broader early-type galaxy population.
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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 | 0.004 |
| 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 | 0.001 |
| 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".