Coport: A New Public Code for Polarized Radiative Transfer in a Covariant Framework$^\spadesuit$
Jiewei Huang, Liheng Zheng, Minyong Guo, Bin Chen

TL;DR
Coport is a new Julia-based public code for covariant polarized radiative transfer in curved spacetime, enabling precise modeling of black hole environments by directly solving coupled equations without separating spacetime and plasma effects.
Contribution
It introduces Coport, a novel, efficient, and accurate code for covariant polarized radiative transfer that integrates gravity and plasma effects without separation, validated against multiple benchmarks.
Findings
Validated against analytical solutions
Accurately models thin and thick disk scenarios
Eliminates need for separate spacetime and plasma computations
Abstract
General relativistic radiative transfer calculations are essential for comparing theoretical models of black hole accretion flows and jets with observational data. In this work, we introduce Coport, a novel public code specifically designed for covariant polarized ray-tracing radiative transfer computations in any spacetime. Written in Julia, Coport includes an interface for visualizing numerical results obtained from HARM, a publicly available implementation of the general relativistic magnetohydrodynamics code. We validate the precision of our code by comparing its outputs with the results from a variety of established methodologies. This includes the verification against analytical solutions, the validation through thin-disk assessments, and the evaluation via thick-disk analyses. Notably, our code employs a methodology that eliminates the need for separating the computations of…
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.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMatrix Theory and Algorithms · Statistical and numerical algorithms · Spatial and Panel Data Analysis
