Furax: A Modular JAX Framework for Linear Operators in Astrophysical and Cosmological Data Analysis
Pierre Chanial, Simon Biquard, Wassim Kabalan, Wuhyun Sohn, Artem Basyrov, Benjamin Beringue, Alexandre Boucaud, Andr\'ea Landais, Magdy Morshed, Radek Stompor, Ema Tsang King Sang, Amalia Villarrubia-Aguilar, Josquin Errard

TL;DR
Furax is an open-source JAX-based framework that offers modular linear operators and solvers tailored for astrophysical and cosmological data analysis, facilitating modeling and inverse problem solving.
Contribution
It introduces a modular, extensible Python framework built on JAX for modeling data systems and solving inverse problems in astrophysics and cosmology.
Findings
Provides domain-specific tools for CMB data analysis
Enables efficient modeling of data acquisition systems
Supports complex inverse problem solutions
Abstract
The Framework for Unified and Robust data Analysis with JAX (Furax) is an open-source Python framework for modeling data acquisition systems and solving inverse problems in astrophysics and cosmology. Built on JAX, Furax provides composable building blocks in the form of general-purpose and domain-specific linear operators, along with preconditioners and solvers for their numerical inversion. Domain-specific tools are provided for astrophysical and cosmic microwave background (CMB) data analysisincluding map-making, instrument modeling, and astrophysical component separationwith a modular architecture designed to extend to other fields.
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.
Taxonomy
TopicsComputational Physics and Python Applications · Radio Astronomy Observations and Technology · Astronomy and Astrophysical Research
