In this era of exoplanet characterisation with JWST, the need for a fast implementation of classical forward models to understand the chemical and physical processes in exoplanet atmospheres is more important than ever. Notably, the time-dependent ordinary differential equations to be solved by chemical kinetics codes are very time-consuming to compute. In this study, we focus on the implementation of neural networks to replace mathematical frameworks in one-dimensional chemical kinetics codes. Using the gravity profile, temperature-pressure profiles, initial mixing ratios, and stellar flux of a sample of hot-Jupiters atmospheres as free parameters, the neural network is built to predict the mixing ratio outputs in steady state. The architecture of the network is composed of individual autoencoders for each input variable to reduce the input dimensionality, which is then used as the input training data for an LSTM-like neural network. Results show that the autoencoders for the mixing ratios, stellar spectra, and pressure profiles are exceedingly successful in encoding and decoding the data. Our results show that in 90% of the cases, the fully trained model is able to predict the evolved mixing ratios of the species in the hot-Jupiter atmosphere simulations. The fully trained model is ~1000 times faster than the simulations done with the forward, chemical kinetics model while making accurate predictions.

翻译：在JWST对系外行星进行表征的时代，快速实现经典正向模型以理解系外行星大气中化学和物理过程的需求比以往任何时候都更为重要。尤其是，化学动力学代码所需求解的时间依赖常微分方程计算非常耗时。在本研究中，我们聚焦于利用神经网络替代一维化学动力学代码中的数学框架。以热木星大气样本的重力剖面、温度-压力剖面、初始混合比和恒星通量作为自由参数，构建神经网络来预测稳态下的混合比输出。网络架构由针对每个输入变量的独立自编码器组成，用于降低输入维度，随后将这些降维数据作为类似LSTM神经网络的输入训练数据。结果表明，针对混合比、恒星光谱和压力剖面的自编码器在编码和解码数据方面极为成功。我们的研究显示，在90%的情况下，充分训练的模型能够准确预测热木星大气模拟中物种的演化混合比。与正向化学动力学模型模拟相比，该充分训练模型的预测速度约快1000倍，同时保持预测精度。