SS 433 is one of the most extreme Galactic x-ray binaries, launching semirelativistic 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 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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• X ray astronomy
• Accretion disk & black-hole plasma
• Astronomical black holes
• Binary stars
• Bayesian methods
• Machine learning