Carbox: an end-to-end differentiable astrochemical simulation framework

TL;DR

Carbox is a novel astrochemical simulation framework that uses Jax for high-performance, differentiable modeling, enabling efficient GPU acceleration, sensitivity analysis, and integration with scientific machine learning for complex molecular systems.

Contribution

The paper introduces Carbox, a new astrochemical simulation tool leveraging Jax for differentiability and GPU acceleration, addressing the need for complex reaction network modeling.

Findings

Carbox achieves high computational efficiency with GPU support.

It enables sensitivity and uncertainty analysis in astrochemical models.

The framework facilitates integration with scientific machine learning techniques.

Abstract

Since the first observations of interstellar molecules, astrochemical simulations have been employed to model and understand its formation and destruction path- ways. With the advent of high-resolution telescopes such as JWST and ALMA, the number of detected molecules has increased significantly, thereby creating a need for increasingly complex chemical reaction networks. To model such complex systems, we have developed Carbox, a new astrochemical simulation code that leverages the modern high-performance transformation framework Jax. With Jax enabling computational efficiency and differentiability, Carbox can easily utilize GPU acceleration, be used to study sensitivity and uncertainty, and interface with advances in Scientific Machine Learning. All of these features are crucial for modeling the molecules observed by current and next-generation telescopes.