Simulation-calibrated Bayesian inference for progenitor properties of the microquasar SS 433
Bibliographic record
Abstract
SS$\,$433 is one of the most extreme Galactic X-ray binaries, launching semi-relativistic jets and showing clear signs of super-critical accretion onto what is likely a black hole. Yet the properties of the binary system that produced it remain uncertain. To solve the inverse problem of inferring the progenitor properties of binaries that evolve into SS$\,$433-like systems, we use an iterative, simulation-based calibration framework that combines Bayesian inference with the isolated binary-evolution code COSMIC. Using six measured properties of SS$\,$433 and the dynamic nested sampler $\texttt{dynesty}$, we explore a ten-dimensional space of possible progenitor masses, orbits, mass-transfer histories, and natal-kick velocities. This approach identifies the regions of parameter space most consistent with SS$\,$433 and allows us to iteratively refine the resulting progenitor distributions. We find 90% confidence intervals for the progenitor initial primary mass of (8, 11) M$_\odot$, secondary mass of (32, 40) M$_\odot$, orbital period of (136, 2259) days, eccentricity of (0.26, 0.6), common-envelope efficiency of (0.44, 0.76), accreted fraction during stable mass transfer of (0.22, 0.6), and black-hole natal-kick magnitude of (5, 68) km/s. These results show that direct probabilistic inference of X-ray binary progenitors can yield new constraints on the formation of extreme accretion systems like SS$\,$433, which has important implications for theoretical expectations of the population of SS$\,$433-like systems in the Galaxy and their connection with cosmic ray observations.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| 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".