GPU-based Monte Carlo dust radiative transfer scheme applied to AGN
Frank Heymann, Ralf Siebenmorgen

TL;DR
This paper introduces a GPU-accelerated 3D Monte Carlo radiative transfer code for modeling dust in AGN, enabling high-resolution, efficient simulations of complex dust geometries and their spectral energy distributions.
Contribution
The paper presents a novel GPU-based Monte Carlo radiative transfer scheme that efficiently models dust in AGN with complex geometries, incorporating stochastic heating and anisotropic scattering.
Findings
Successfully reproduces benchmark SEDs and dust temperatures.
Models AGN torus with clumpy dust and variable viewing angles.
Fits observed quasar SED with combined AGN and cirrus components.
Abstract
A three dimensional parallel Monte Carlo (MC) dust radiative transfer code is presented. To overcome the huge computing time requirements of MC treatments, the computational power of vectorized hardware is used, utilizing either multi-core computer power or graphics processing units. The approach is a self-consistent way to solve the radiative transfer equation in arbitrary dust configurations. The code calculates the equilibrium temperatures of two populations of large grains and stochastic heated polycyclic aromatic hydrocarbons (PAH). Anisotropic scattering is treated applying the Heney-Greenstein phase function. The spectral energy distribution (SED) of the object is derived at low spatial resolution by a photon counting procedure and at high spatial resolution by a vectorized ray-tracer. The latter allows computation of high signal-to-noise images of the objects at any frequencies…
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.
