Flow Matching Posterior Estimation for Simulation-based Atmospheric Retrieval of Exoplanets

Journal

The characterization of exoplanetary atmospheres allows a deeper understanding of planetary formation, evolution, and habitability through atmospheric retrieval, which consists in inferring various properties of exoplanetary atmospheres given their spectroscopic observations. Traditional atmospheric retrieval methods based on Bayesian inference, such as Nested Sampling, require significant computational resources to compute the full posterior distribution of atmospheric parameters, limiting their scalability for future large-scale surveys and high-resolution characterizations. Additionally, the rise of modern density estimation techniques poses a fundamental need for comprehensive evaluation frameworks to objectively compare the posterior distributions of heterogeneous probabilistic estimators. Within the scope of the 2023 edition of the Ariel Data Challenge, this work proposes a novel, scalable atmospheric retrieval framework based on Flow Matching Posterior Estimation (FMPE) and Continuous Normalizing Flows (CNFs), leveraging transmission spectra, instrumental uncertainties across wavelength channels, and auxiliary information about planetary systems, to retrieve the posterior distribution of atmospheric parameters in a significantly reduced computational time compared to conventional techniques. Through the fair definition of an extensive posterior evaluation framework, our approach demonstrates superior performance in target prediction accuracy, uncertainty quantification, calibration, and posterior coverage—consistently outperforming existing neural- and sampling-based retrieval methods. In addition, complementary ablation studies emphasize the value of incorporating auxiliary planetary system data, enhancing the reliability, explainability, and interpretability of atmospheric inferences. Together, these contributions establish the proposed approach as a robust, scalable, and adaptable framework for the analysis of exoplanetary atmospheres.

Description

The paper titled “Flow Matching Posterior Estimation for Simulation-Based Atmospheric Retrieval of Exoplanets” introduces an advanced machine learning framework to accelerate and improve the characterization of exoplanetary atmospheres.

The proposed approach, based on Flow Matching Posterior Estimation and Continuous Normalizing Flows, enables fast and accurate retrieval of atmospheric parameters from spectroscopic observations while maintaining rigorous uncertainty estimation.

Validated within the 2023 Ariel Data Challenge, the method substantially reduces computational costs compared to traditional Bayesian sampling techniques and exhibits superior performance in accuracy, calibration, and posterior coverage. By integrating auxiliary planetary data to enhance interpretability, this framework represents a major step forward toward scalable, data-driven exploration of planetary atmospheres and habitability.