Fast GPU-Powered and Auto-Differentiable Forward Modeling of IFU Data Cubes

TL;DR

RUBIX is an open-source GPU-accelerated, auto-differentiable tool for fast forward modeling of galaxy IFU data cubes, enabling efficient simulations and gradient-based parameter optimization.

Contribution

It introduces RUBIX, a modular, GPU-accelerated, auto-differentiable software in JAX for modeling galaxy IFU data from simulations, significantly improving performance and enabling new analysis methods.

Findings

Performance improved by a factor of 600 over existing codes.

Compute times reduced from hours to seconds.

Enables gradient-based optimization of astrophysical models.

Abstract

We present RUBIX, a fully tested, well-documented, and modular Open Source tool developed in JAX, designed to forward model IFU cubes of galaxies from cosmological hydrodynamical simulations. The code automatically parallelizes computations across multiple GPUs, demonstrating performance improvements over state-of-the-art codes by a factor of 600. This optimization reduces compute times from hours to only seconds. RUBIX leverages JAX's auto-differentiation capabilities to enable not only forward modeling but also gradient computations through the entire pipeline paving the way for new methodological approaches such as e.g. gradient-based optimization of astrophysics model parameters. RUBIX is open-source and available on GitHub: https://github.com/ufuk-cakir/rubix.