A New Emulated Monte Carlo Radiative Transfer Disk-Wind Model: X-Ray Accretion Disk-wind Emulator -- XRADE
Abstract
We present a new X-Ray Accretion Disk-wind Emulator (\textsc{xrade}) based on the 2.5D Monte Carlo radiative transfer code which provides a physically-motivated, self-consistent treatment of both absorption and emission from a disk-wind by computing the local ionization state and velocity field within the flow. \textsc{xrade} is then implemented through a process that combines X-ray tracing with supervised machine learning. We develop a novel emulation method consisting in training, validating, and testing the simulated disk-wind spectra into a purposely built artificial neural network. The trained emulator can generate a single synthetic spectrum for a particular parameter set in a fraction of a second, in contrast to the few hours required by a standard Monte Carlo radiative transfer pipeline. The emulator does not suffer from interpolation issues with multi-dimensional spaces that are typically faced by traditional X-ray fitting packages such as \textsc{xspec}. \textsc{xrade} will be suitable to a wide number of sources across the black-hole mass, ionizing luminosity, and accretion rate scales. As an example, we demonstrate the applicability of \textsc{xrade} to the physical interpretation of the X-ray spectra of the bright quasar PDS 456, which hosts the best-established accretion-disk wind observed to date. We anticipate that our emulation method will be an indispensable tool for the development of high-resolution theoretical models, with the necessary flexibility to be optimized for the next generation micro-calorimeters on board future missions, like \textit{XRISM/resolve} and \textit{Athena/X-IFU}. This tool can also be implemented across a wide variety of X-ray spectral models and beyond.
Keywords
Cite
@article{arxiv.2207.13731,
title = {A New Emulated Monte Carlo Radiative Transfer Disk-Wind Model: X-Ray Accretion Disk-wind Emulator -- XRADE},
author = {G. A. Matzeu and M. Lieu and M. T. Costa and J. N. Reeves and V. Braito and M. Dadina and E. Nardini and P. G. Boorman and M. L. Parker and S. A. Sim and D. Barret and E. Kammoun and R. Middei and M. Giustini and M. Brusa and J. Pérez Cabrera and S. Marchesi},
journal= {arXiv preprint arXiv:2207.13731},
year = {2022}
}
Comments
20 pages, 17 figures, MNRAS accepted for publication