Self-consistent modelling of line-driven hot-star winds with Monte Carlo radiation hydrodynamics

TL;DR

This paper introduces a new Monte Carlo radiation hydrodynamics method for self-consistently modeling line-driven winds in hot stars, effectively handling multi-line effects and multidimensional configurations.

Contribution

The paper presents a novel coupling of Monte Carlo radiative transfer with fluid dynamics to model hot-star winds self-consistently, improving upon existing methods.

Findings

Validated the approach with 1D Sobolev-type wind calculations.

Compared results with CAK theory predictions, showing good agreement.

Demonstrated diagnostic capabilities and discussed limitations and future extensions.

Abstract

Radiative pressure exerted by line interactions is a prominent driver of outflows in astrophysical systems, being at work in the outflows emerging from hot stars or from the accretion discs of cataclysmic variables, massive young stars and active galactic nuclei. In this work, a new radiation hydrodynamical approach to model line-driven hot-star winds is presented. By coupling a Monte Carlo radiative transfer scheme with a finite-volume fluid dynamical method, line-driven mass outflows may be modelled self-consistently, benefiting from the advantages of Monte Carlo techniques in treating multi-line effects, such as multiple scatterings, and in dealing with arbitrary multidimensional configurations. In this work, we introduce our approach in detail by highlighting the key numerical techniques and verifying their operation in a number of simplified applications, specifically in a series of self-consistent, one-dimensional, Sobolev-type, hot-star wind calculations. The utility and accuracy of our approach is demonstrated by comparing the obtained results with the predictions of various formulations of the so-called CAK theory and by confronting the calculations with modern sophisticated techniques of predicting the wind structure. Using these calculations, we also point out some useful diagnostic capabilities our approach provides. Finally we discuss some of the current limitations of our method, some possible extensions and potential future applications.