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

2024/12/11 by Ufuk Çakır, Çakır, Ufuk, Anna Lena Schaible +3 · 2 citations
Computer Science · Engineering · #Astrophysics of Galaxies (astro-ph.GA) #Computational Physics (physics.comp-ph) #Data Analysis #Embedded Systems Design Techniques #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Parallel Computing and Optimization Techniques #Real-time simulation and control systems #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.2412.08265

openalex publication_date 2024/12/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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