Kratos-polrad: Novel GPU system for Monte-Carlo simulations with consistent polarization calculations

TL;DR

Kratos-polrad is a GPU-accelerated Monte Carlo radiative transfer code that accurately models polarization in astrophysical environments, offering significant performance improvements for complex polarimetric simulations.

Contribution

It introduces a novel GPU-based framework for self-consistent polarization calculations in radiative transfer, utilizing quaternion algebra and optimized memory access.

Findings

Achieves ~100x speedup over CPU methods.

Validates accurate polarization modeling against analytical solutions.

Effectively simulates complex magnetic field and dust grain alignment scenarios.

Abstract

Polarized radiation serves as a vital diagnostic tool in astrophysics, providing unique insights into magnetic field geometries, scattering processes, and three-dimensional structures in diverse astrophysical scenarios. To address these applications, we present Kratos-polrad, a novel GPU-accelerated Monte Carlo Radiative Transfer code built upon the heterogeneous computing framework of Kratos, designed for self-consistent and efficient polarization calculations. It utlizes comprehensive treatment of Stokes parameters throughout photon propagation, featuring transforms the grain-lab frame transforms using quaternion algebra and consistent non-linear polarization extinction in cells, which are useful in modeling radiative transfer processes with scatterings by aligned dust grains. The code implements two-step polarimetry imaging that decouples Monte Carlo sampling of scattering physics from imaging geometry, enabling efficient synthesis maximizing the utilization of photon packets. Extensive validation against analytical solutions and established codes demonstrates accurate treatment of diverse polarization phenomena, including self-scattering polarization, dichroic extinction in aligned dust grains, and complex polarization patterns in twisted magnetic field configurations. By leveraging massive GPU parallelism, optimized memory access patterns, and analytical approaches for optically thick cells, Kratos-polrad achieves performance improvements of $\sim 10^{2}$ times compared to CPU-based methods, enabling previously prohibitive studies in polarimetric astrophysics.