We describe a machine-learning-based surrogate model for reproducing the Bayesian posterior distributions for exoplanet atmospheric parameters derived from transmission spectra of transiting planets with typical retrieval software such as TauRex. The model is trained on ground truth distributions for seven parameters: the planet radius, the atmospheric temperature, and the mixing ratios for five common absorbers: $H_2O$, $CH_4$, $NH_3$, $CO$ and $CO_2$. The model performance is enhanced by domain-inspired preprocessing of the features and the use of semi-supervised learning in order to leverage the large amount of unlabelled training data available. The model was among the winning solutions in the 2023 Ariel Machine Learning Data Challenge.

翻译：我们描述了一种基于机器学习的替代模型，用于重现通过透射光谱（来自凌星系外行星）使用典型反演软件（如TauRex）获得的系外行星大气参数贝叶斯后验分布。该模型在七个参数的真值分布上进行训练：行星半径、大气温度以及五种常见吸收体的混合比：$H_2O$、$CH_4$、$NH_3$、$CO$ 和 $CO_2$。通过领域启发式的特征预处理以及利用大量可用未标记训练数据的半监督学习，模型性能得到提升。该模型是2023年Ariel机器学习数据挑战赛的获奖方案之一。