Kratos-linerad: GPU-accelerated Monte Carlo radiative transfer of lines with efficient imaging

[2608.09340] Kratos-linerad: GPU-accelerated Monte Carlo radiative transfer of lines with efficient imaging Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Astrophysics > Instrumentation and Methods for Astrophysics arXiv:2608.09340 (astro-ph) [Submitted on 10 Aug 2026 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: Kratos-linerad: GPU-accelerated Monte Carlo radiative transfer of lines with efficient imaging Authors: Lile Wang View a PDF of the paper titled Kratos-linerad: GPU-accelerated Monte Carlo radiative transfer of lines with efficient imaging, by Lile Wang View PDF HTML (experimental) Abstract: Spectral lines encode the velocity, the temperature, and the chemical structure of astrophysical gas; interpreting them requires radiative transfer that is accurate at high optical depth, consistent with the local excitation, and efficient enough to synthesize velocity-resolved images. We present Kratos-linerad, a GPU-accelerated Monte Carlo radiative transfer code for spectral lines. The level populations can be iterated to statistical equilibrium together with the escaping photon distribution, treating angle-dependent partial frequency redistribution through constant-memory sampling tables. The code adopts the two-step imaging scheme to line transfer, in which a Monte Carlo pass samples a velocity-resolved scattering emissivity, and a deterministic ray-tracing pass synthesizes channel maps decoupled from the scattering geometry. Validation reproduces the analytic scaling of escaped spectra and the imaging double-peak profiles. GPU parallelism enables both stages sufficiently fast for routine application to astrophysically realistic models. Comments: 13 pages, 7 figures, to be submitted Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM) ; Astrophysics of Galaxies (astro-ph.GA); Solar and Stellar Astrophysics (astro-ph.SR) Cite as: arXiv:2608.09340 [astro-ph.IM] (or arXiv:2608.09340v2 [astro-ph.IM] for this version) https://doi.org/10.48550/arXiv.2608.09340 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Lile Wang [ view email ] [v1] Mon, 10 Aug 2026 09:20:47 UTC (846 KB) [v2] Sat, 15 Aug 2026 09:29:21 UTC (846 KB) Full-text links: Access Paper: View a PDF of the paper titled Kratos-linerad: GPU-accelerated Monte Carlo radiative transfer of lines with efficient imaging, by Lile Wang View PDF HTML (experimental) TeX Source view license Current browse context: astro-ph.IM < prev | next > new | recent | 2026-08 Change to browse by: astro-ph astro-ph.GA astro-ph.SR References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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