THE X-RAY LUMINOSITY FUNCTIONS OF FIELD LOW-MASS X-RAY BINARIES IN EARLY-TYPE GALAXIES: EVIDENCE FOR A STELLAR AGE DEPENDENCE
Bibliographic record
Abstract
We present direct constraints on how the formation of low-mass X-ray binary (LMXB) populations in galactic fields depends on stellar age. In this pilot study, we utilize Chandra and Hubble Space Telescope ( HST ) data to detect and characterize the X-ray point source populations of three nearby early-type galaxies: NGC 3115, 3379, and 3384. The luminosity-weighted stellar ages of our sample span ≈3–10 Gyr. X-ray binary population synthesis models predict that the field LMXBs associated with younger stellar populations should be more numerous and luminous per unit stellar mass than older populations due to the evolution of LMXB donor star masses. Crucially, the combination of deep Chandra and HST observations allows us to test directly this prediction by identifying and removing counterparts to X-ray point sources that are unrelated to the field LMXB populations, including LMXBs that are formed dynamically in globular clusters, Galactic stars, and background active galactic nuclei/galaxies. We find that the "young" early-type galaxy NGC 3384 (≈2–5 Gyr) has an excess of luminous field LMXBs ( L X ≳ (5–10) × 10 37 erg s −1 ) per unit K -band luminosity ( L K ; a proxy for stellar mass) than the "old" early-type galaxies NGC 3115 and 3379 (≈8–10 Gyr), which results in a factor of ≈2–3 excess of L X / L K for NGC 3384. This result is consistent with the X-ray binary population synthesis model predictions; however, our small galaxy sample size does not allow us to draw definitive conclusions on the evolution field LMXBs in general. We discuss how future surveys of larger galaxy samples that combine deep Chandra and HST data could provide a powerful new benchmark for calibrating X-ray binary population synthesis models.
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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 | 0.000 | 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) | 0.006 | 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".