Simulation-calibrated Bayesian inference for progenitor properties of the microquasar SS 433
Nathan Steinle, Matthew Mould, Sarah Al-Humaikani, Austin MacMaster, Brydyn Mac Intyre, Samar Safi-Harb

TL;DR
This paper develops a Bayesian inference framework combined with binary evolution simulations to constrain the progenitor properties of the extreme X-ray binary SS 433, providing new insights into its formation and population in the galaxy.
Contribution
It introduces a simulation-calibrated Bayesian method to infer progenitor parameters of SS 433-like systems, refining previous models with iterative, data-driven analysis.
Findings
Progenitor primary mass: 8-11 M_sun
Secondary mass: 32-40 M_sun
Black-hole natal kick: 5-68 km/s
Abstract
SS433 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 SS433-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 SS433 and the dynamic nested sampler , 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 SS433 and allows us to iteratively refine the resulting progenitor…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
