Gaussian Process Inference of Stochastic Magneto-Active Dynamics and Viscosity in Swift J1727.8-1613

Abstract

Linking X-ray variability to the underlying magnetohydrodynamic (MHD) dynamics of black hole X-ray binaries remains challenging. We systematically investigate the stochastic and oscillatory variability of the black hole X-ray binary candidate Swift J1727.8$-$1613 during its 2023 outburst using Gaussian process (GP) regression applied to Insight-HXMT multi-band light curves. The variability is modeled with a physically motivated composite kernel comprising one stochastically driven damped simple harmonic oscillator (SHO) and two damped random walk (DRW) components. The SHO term robustly recovers quasi-periodic oscillations (QPOs) with frequencies $\nu_0 \sim 0.07$--$5$ Hz, consistent with the fundamental Alfv\'en mode of a contracting magnetically confined disk--coronal cavity. The quality factor rises from $Q \sim 3$ to $Q \sim 10$, suggesting increasing coherence of the magnetic cavity. We also find an anti-correlation between QPO frequency and the short DRW damping timescale, supporting our proposed stochastic magneto-active dynamics scenario. Associating the short and long DRW timescales with the local turbulent turnover and thermal adjustment timescales, respectively, we infer an effective viscosity parameter of $\alpha \approx 0.1$, supporting a strongly magnetized accretion flow. Strikingly, near the onset of relativistic jet ejection around MJD 60206, both relaxation timescales collapse toward the 0.1 s sampling limit, suggesting a rapid reorganization of the disk internal energy balance immediately before jet launching. Our results establish GP inference as a powerful route to connecting X-ray timing observables with the dynamical state of black hole accretion flows.