FRECKLL：全量及约化系外行星化学动力学蒸馏代码
FRECKLL: Full and Reduced Exoplanet Chemical Kinetics distiLLed
AI总结：
本文提出Python一维化学动力学代码FRECKLL，通过蒸馏方法计算反应速率以最小化误差并显著加速求解，并集成Venot2020网络与TauREx 3.1插件，验证了JWST对系外行星非平衡化学的反演能力。
摘要：
我们介绍一个新的Python一维化学动力学代码FRECKLL（Full and Reduced Exoplanet Chemical Kinetics distiLLed），用于高效演化大型化学网络。FRECKLL在计算反应速率时采用“蒸馏”方法，将误差界限最小化至双精度数值所允许的最小值（$ε\leq 10^{-15}$）。与使用成对求和等传统算法的速率求和相比，蒸馏方法在全量和约化网络中都使求解器时间减少了十倍。FRECKLL打包了全量和约化的Venot2020网络，并提供一个TauREx 3.1插件，用于系外行星大气的前向建模和反演。我们展示了利用全量和约化Venot2020化学网络对模拟的HD189733 JWST光谱进行的TauREx反演，证明了完全非平衡化学反演的可行性以及JWST探测非平衡过程的能力。
英文摘要：
We introduce a new Python 1D chemical kinetic code FRECKLL (Full and Reduced Exoplanet Chemical Kinetics distiLLed) to evolve large chemical networks efficiently. FRECKLL employs `distillation' in computing the reaction rates, which minimizes the error bounds to the minimum allowed by double precision values ($ε\leq 10^{-15}$). Compared to summation of rates with traditional algorithms like pairwise summation, distillation provides a tenfold reduction in solver time for both full and reduced networks. Both the full and reduced Venot2020 networks are packaged in FRECKLL as well as a TauREx 3.1 plugin for usage in forward modelling and retrievals of exoplanet atmospheres. We present TauREx retrievals performed on a simulated HD189733 JWST spectra using the full and reduced Venot2020 chemical networks and demonstrate the viability of total disequilibrium chemistry retrievals and the ability for JWST to detect disequilibrium processes.
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