Efficient reduction of stellar contamination and noise in planetary transmission spectra using neural networks

TL;DR

This paper introduces a neural network-based method using denoising autoencoders to efficiently reduce stellar contamination and noise in exoplanet transmission spectra, enhancing atmospheric retrieval accuracy and computational efficiency.

Contribution

We develop and validate an unsupervised denoising autoencoder approach that improves spectral correction in exoplanet studies, outperforming traditional methods in accuracy and speed.

Findings

Autoencoders accurately reconstruct uncontaminated spectra at low S/N.

Denoising reduces bias in atmospheric parameter retrievals.

Method decreases computational time by a factor of three to six.

Abstract

Context: JWST has enabled transmission spectroscopy at unprecedented precision, but stellar heterogeneities (spots and faculae) remain a dominant contamination source that can bias atmospheric retrievals if uncorrected. Aims: We present a fast, unsupervised methodology to reduce stellar contamination and instrument-specific noise in exoplanet transmission spectra using denoising autoencoders, improving the reliability of retrieved atmospheric parameters. Methods: We design and train denoising autoencoder architectures on large synthetic datasets of terrestrial (TRAPPIST-1e analogues) and sub-Neptune (K2-18b analogues) planets. Reconstruction quality is evaluated with the $\chi^2$ statistic over a wide range of signal-to-noise ratios, and atmospheric retrieval experiments on contaminated spectra are used to compare against standard correction approaches in accuracy and computational cost. Results: The autoencoders reconstruct uncontaminated spectra while preserving key molecular features, even at low S/N. In retrieval tests, pre-processing with denoising autoencoders reduces bias in inferred abundances relative to uncorrected baselines and matches the accuracy of simultaneous stellar-contamination fitting while reducing computational time by a factor of three to six. Conclusions: Denoising autoencoders provide an efficient alternative to conventional correction strategies and are promising components of future atmospheric characterization pipelines for both rocky and gaseous exoplanets.