Constraining Progenitors of Observed Low-mass X-ray Binaries Using Convection and Rotation-Boosted Magnetic Braking
Bibliographic record
Abstract
Abstract We present a new method for constraining the mass transfer evolution of low-mass X-ray binaries (LMXBs)—a reverse population synthesis technique. This is done using the detailed 1D stellar evolution code MESA (Modules for Experiments in Stellar Astrophysics) to evolve a high-resolution grid of binary systems spanning a comprehensive range of initial donor masses and orbital periods. We use the recently developed convection and rotation-boosted (CARB) magnetic braking scheme. The CARB magnetic braking scheme is the only magnetic braking prescription capable of reproducing an entire sample of well-studied persistent LMXBs—those with mass ratios, periods, and mass transfer rates that have been observationally determined. Using the reverse population synthesis technique, where we follow any simulated system that successfully reproduces an observed LMXB backward, we have constrained possible progenitors for each observed well-studied persistent LMXB. We also determined that the minimum number of LMXB formations in the Milky Way is 1500 per Gyr if we exclude Cyg X-2. For Cyg X-2, the most likely formation rate is 9000 LMXB Gyr−1. The technique we describe can be applied to any observed LMXB with well-constrained mass ratio, period, and mass transfer rate. With the upcoming GAIA DR3 containing information on binary systems, this technique can be applied to the data release to search for progenitors of observed persistent LMXBs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".