Data-driven statistical model of nonthermal particle acceleration by the kink instability in relativistic jets

Relativistic astrophysical jets are among the most powerful particle accelerators in the Universe. Recent works have shown that one of the potential mechanisms of particle acceleration is the development of magnetohydrodynamic kink instabilities in the jet, which can efficiently convert the jet's magnetic energy into nonthermal particles. However, the physics that controls the structure of the energy spectrum of nonthermal particles remains poorly understood. In this work, we develop a statistical Fokker-Planck-type model of nonthermal particle acceleration driven by the kink instability in jets. We utilize a novel machine learning-based approach to infer this statistical model from the phase-space dynamics of the accelerated particles. We characterize the time- and energy-dependent advective and diffusive acceleration processes that occur in the nonlinear phase of the kink instability, and quantify the rate at which particles escape from the acceleration region. Our results shed new light on the physical processes that govern the structure of the energy spectrum of accelerated particles in kink-unstable jets.