  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.

Earth and Planetary Astrophysics;Instrumentation and Methods for Astrophysics;Machine Learning;Data Analysis, Statistics and Probability