# An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval

arXiv: https://arxiv.org/abs/1905.10659 · HTML: https://mlsanity.com/paper/1905.10659/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2019.

## Built on by (most cited first, 16 of 16)

- [Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift](https://mlsanity.com/paper/2306.12306/) (2023)
- [Analytical Modelling of Exoplanet Transit Specroscopy with Dimensional Analysis and Symbolic Regression](https://mlsanity.com/paper/2112.11600/) (2021)
- [Low-Budget Simulation-Based Inference with Bayesian Neural Networks](https://mlsanity.com/paper/2408.15136/) (2024)
- [Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra](https://mlsanity.com/paper/2201.02696/) (2022)
- [Using a neural network approach to accelerate disequilibrium chemistry calculations in exoplanet atmospheres](https://mlsanity.com/paper/2306.07074/) (2023)
- [Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection](https://mlsanity.com/paper/2308.07604/) (2023)
- [Flow Matching for Atmospheric Retrieval of Exoplanets: Where Reliability meets Adaptive Noise Levels](https://mlsanity.com/paper/2410.21477/) (2024)
- [Intercomparison of Brown Dwarf Model Grids and Atmospheric Retrieval Using Machine Learning](https://mlsanity.com/paper/2305.07719/) (2023)
- [Towards a Probabilistic Fusion Approach for Robust Battery Prognostics](https://mlsanity.com/paper/2405.15292/) (2024)
- [Traceable Spectral Inference via Influence Functions: Efficient Data Attribution and Error Proxies for the Ariel Mission](https://mlsanity.com/paper/2608.23458/) (2026)
- [Predicting Exoplanetary Features with a Residual Model for Uniform and Gaussian Distributions](https://mlsanity.com/paper/2406.10771/) (2024)
- [Uncertainties of Satellite-based Essential Climate Variables from Deep Learning](https://mlsanity.com/paper/2412.17506/) (2024)
- [Exoplanetary atmospheres retrieval via a quantum extreme learning machine](https://mlsanity.com/paper/2509.03617/) (2025)
- [Hunting for "Oddballs" with Machine Learning: Detecting Anomalous Exoplanets Using a Deep-Learned Low-Dimensional Representation of Transit Spectra with Autoencoders](https://mlsanity.com/paper/2601.02324/) (2026)
- [Life, Machine Learning, and the Search for Habitability: Predicting Biosignature Fluxes for the Habitable Worlds Observatory](https://mlsanity.com/paper/2601.12557/) (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)

Machine learning is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using machine learning algorithms for atmospheric retrieval by implementing a random forest to perform retrievals in seconds that are consistent with the traditional, computationally-expensive nested-sampling retrieval method. We expand upon their approach by presenting a new machine learning model, textttplan-net, based on an ensemble of Bayesian neural networks that yields more accurate inferences than the random forest for the same data set of synthetic transmission spectra. We demonstrate that an ensemble provides greater accuracy and more robust uncertainties than a single model. In addition to being the first to use Bayesian neural networks for atmospheric retrieval, we also introduce a new loss function for Bayesian neural networks that learns correlations between the model outputs. Importantly, we show that designing machine learning models to explicitly incorporate domain-specific knowledge both improves performance and provides additional insight by inferring the covariance of the retrieved atmospheric parameters. We apply textttplan-net to the Hubble Space Telescope Wide Field Camera 3 transmission spectrum for WASP-12b and retrieve an isothermal temperature and water abundance consistent with the literature. We highlight that our method is flexible and can be expanded to higher-resolution spectra and a larger number of atmospheric parameters.