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.

用机器学习建立的代理模型，能够生成类似于使用传统检索软件（如 TauRex）从行星凌星光谱中得出的行星大气参数的贝叶斯后验分布。该模型通过对七个参数（行星半径、大气温度以及五种常见吸收剂的混合比：$H_2O$、$CH_4$、$NH_3$、$CO$ 和 $CO_2$）的真实分布进行预训练，采用问题领域的启发式预处理和半监督学习方法提高模型性能，并在 2023 年 Ariel 机器学习数据挑战中获得优胜方案之一。