RUBIX: Differentiable forward modelling of galaxy spectral data cubes for gradient-based parameter estimation
Abstract
Although integral-field spectroscopy enables spatially resolved spectral studies of galaxies, bridging particle-based simulations to observations remains slow and non-differentiable. We present RUBIX, a JAX-based pipeline that models mock integral-field unit (IFU) cubes for galaxies end-to-end and calculates gradients with respect to particle inputs. Our implementation is purely functional, sharded, and differentiable throughout. We validate the gradients against central finite differences and demonstrate gradient-based parameter estimation on controlled setups. While current experiments are limited to basic test cases, they demonstrate the feasibility of differentiable forward modelling of IFU data. This paves the way for future work scaling up to realistic galaxy cubes and enabling machine learning workflows for IFU-based inference. The source code for the RUBIX software is publicly available under https://github.com/AstroAI-Lab/rubix.
Keywords
Cite
@article{arxiv.2511.17110,
title = {RUBIX: Differentiable forward modelling of galaxy spectral data cubes for gradient-based parameter estimation},
author = {Anna Lena Schaible and Ufuk Çakır and Tobias Buck and Harald Mack and Aura Obreja and Nihat Oguz and William H. Oliver and Horea-Alexandru Cărămizaru},
journal= {arXiv preprint arXiv:2511.17110},
year = {2025}
}
Comments
Accepted to 1st Workshop on Differentiable Systems and Scientific Machine Learning @ EurIPS 2025, 6 pages, 3 figures