利用流体动力学、化学与辐射传输实现分子光谱的端到端可微检索
End-to-end differentiable retrieval of molecular spectra using hydrodynamics, chemistry, and radiative transfer
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中文总结 AI 辅助
该研究开发了一套端到端可微的Jax流程，耦合流体动力学、化学与辐射传输，可从分子线光谱恢复流体动力学激波模型参数并优化化学反应速率系数，实现耦合物理化学模型的高效优化。
中文摘要 AI 辅助
我们旨在通过耦合流体动力学、化学与辐射传输的流程重现观测到的分子线发射，该流程可同时优化所有相关物理与化学参数。我们开发了一套端到端可微的Jax流程，包含定制流体动力学代码、可微化学代码Carbox的修改版本，以及定制辐射传输代码。我们使用受控合成数据测试该框架，证明其可直接从分子线光谱中恢复流体动力学激波模型的参数，并通过基于梯度的优化优化选定的化学反应速率系数。该可微公式可在保留完整时变演化的同时，实现耦合物理与化学模型的高效优化。
英文摘要
We aim to reproduce observed molecular line emission using a pipeline that couples hydrodynamics, chemistry, and radiative transfer, capable of simultaneously optimizing all relevant physical and chemical parameters. We developed an end-to-end differentiable Jax pipeline consisting of a custom hydrodynamical code, a modified version of the differentiable chemical code Carbox, and a custom radiative transfer code. We tested the framework using controlled synthetic data. We demonstrate that the framework can recover the parameters of hydrodynamical shock models directly from molecular line spectra and optimize selected chemical reaction rate coefficients through gradient-based optimization. The differentiable formulation enables efficient optimization of the coupled physical and chemical model while preserving the full time-dependent evolution.
