Models of Ultraluminous X‐Ray Sources with Intermediate‐Mass Black Holes
Bibliographic record
Abstract
We have computed models for ultraluminous X-ray sources (ULXs) consisting of a black hole accretor of intermediate mass (IMBH; e.g., ~1000 M ☉ ) and a captured donor star. For each of four different sets of initial donor masses and orbital separations we computed 30,000 binary evolution models using a full Henyey stellar evolution code. To our knowledge, this is the first time that a population of X-ray binaries this large has been carried out with other than approximation methods, and it serves to demonstrate the feasibility of this approach to large-scale population studies of mass transfer binaries. In the present study, we find that in order to have a plausible efficiency for producing active ULX systems with IMBHs having luminosities ≳10 40 ergs s -1 , there are two basic requirements for the capture of companion/donor stars. First, the donor stars should be massive, i.e., ≳8 M ☉ . Second, the initial orbital separations after circularization should be close, i.e., ≲6-30 times the radius of the donor star when on the main sequence. Even under these optimistic conditions, we show that the production rate of IMBH-ULX systems may fall short of the observed values by factors of 10-100.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".