English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We introduce Mahakala, a Python-based, modular, radiative ray-tracing codefor curved space-times. We employ Google's JAX framework for acceleratedautomatic differentiation, which can efficiently compute Christoffel symbolsdirectly from the metric, allowing the user to easily and quickly simulatephoton trajectories through non-Kerr metrics. JAX also enables Mahakala to runin parallel on both CPUs and GPUs and achieve speeds comparable to C-basedcodes. Mahakala natively uses the Cartesian Kerr-Schild coordinate system,which avoids numerical issues caused by the "pole" of spherical coordinates. Wedemonstrate Mahakala's capabilities by simulating the 1.3 mm wavelength images(the wavelength of Event Horizon Telescope observations) of generalrelativistic magnetohydrodynamic simulations of low-accretion rate supermassiveblack holes. The modular nature of Mahakala allows us to easily quantify therelative contribution of different regions of the flow to image features. Weshow that most of the emission seen in 1.3 mm images originates close to theblack hole. We also quantify the relative contribution of the disk, forwardjet, and counter jet to 1.3 mm images.

Mahakala: a Python-based Modular Ray-tracing and Radiative Transfer Algorithm for Curved Space-times