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.
@article{arxiv.2412.08265,
title = {Fast GPU-Powered and Auto-Differentiable Forward Modeling of IFU Data Cubes},
author = {Ufuk Çakır and Anna Lena Schaible and Tobias Buck},
journal= {arXiv preprint arXiv:2412.08265},
year = {2024}
}
Comments
accepted to the Machine Learning and the Physical Sciences Workshop, NeurIPS 2024