Supervised Machine Learning Methods with Uncertainty Quantification for Exoplanet Atmospheric Retrievals from Transmission Spectroscopy

TL;DR

This paper systematically compares various machine learning regression techniques for exoplanet atmospheric retrievals from transmission spectra, focusing on accuracy, speed, and uncertainty quantification, to provide efficient alternatives to Bayesian methods.

Contribution

It evaluates multiple ML algorithms and preprocessing methods, identifying the best combination for exoplanet atmospheric parameter retrievals from transmission spectra.

Findings

Support vector machines and random forests perform best in accuracy and speed.

Preprocessing significantly impacts model performance and uncertainty estimates.

The optimal ML model was validated on JWST observations of WASP-39b.

Abstract

Standard Bayesian retrievals for exoplanet atmospheric parameters from transmission spectroscopy, while well understood and widely used, are generally computationally expensive. In the era of the JWST and other upcoming observatories, machine learning approaches have emerged as viable alternatives that are both efficient and robust. In this paper we present a systematic study of several existing machine learning regression techniques and compare their performance for retrieving exoplanet atmospheric parameters from transmission spectra. We benchmark the performance of the different algorithms on the accuracy, precision, and speed. The regression methods tested here include partial least squares (PLS), support vector machines (SVM), k nearest neighbors (KNN), decision trees (DT), random forests (RF), voting (VOTE), stacking (STACK), and extreme gradient boosting (XGB). We also investigate the impact of different preprocessing methods of the training data on the model performance. We quantify the model uncertainties across the entire dynamical range of planetary parameters. The best performing combination of ML model and preprocessing scheme is validated on a the case study of JWST observation of WASP-39b.