🤖 AI Summary
Stellar surface inhomogeneities, such as spots and faculae, can severely contaminate exoplanetary transmission spectra, leading to significant biases in atmospheric parameter retrieval. This work proposes an unsupervised preprocessing method based on a denoising autoencoder, which—unlike conventional joint-fitting approaches—simultaneously removes stellar contamination and instrumental noise without incurring high computational costs. Trained on synthetic data, the method employs χ² statistics to assess reconstruction fidelity and integrates seamlessly into standard atmospheric retrieval pipelines. Experiments demonstrate that it effectively preserves key molecular features even at low signal-to-noise ratios, yielding retrieval biases substantially lower than those of uncorrected baselines and achieving accuracy comparable to joint fitting, while reducing computational time by a factor of 3–6. The approach is applicable to both rocky and gaseous exoplanets.
📝 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.