# Reproducing Bayesian Posterior Distributions for Exoplanet Atmospheric Parameter Retrievals with a Machine Learning Surrogate Model

arXiv: https://arxiv.org/abs/2310.10521 · HTML: https://mlsanity.com/paper/2310.10521/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Analytical Modelling of Exoplanet Transit Specroscopy with Dimensional Analysis and Symbolic Regression](https://mlsanity.com/paper/2112.11600/) (2021)
- [Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots](https://mlsanity.com/paper/2010.15996/) (2020)
- [Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra](https://mlsanity.com/paper/2201.02696/) (2022)

## Built on by (most cited first, 3 of 3)

- [Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling](https://mlsanity.com/paper/2312.08295/) (2023)
- [ASTER -- Agentic Science Toolkit for Exoplanet Research](https://mlsanity.com/paper/2603.26953/) (2026)
- [Machine Learning and Deep Learning for Exoplanet Detection and Atmospheric Characterization with JWST and the Upcoming Ariel Mission](https://mlsanity.com/paper/2606.23766/) (2026)

## Abstract (arXiv)

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: H2O, CH4, NH3, CO and CO2. 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.