Exoplanets, -moons, -comets

Efficient Reduction Of Stellar Contamination And Noise In Planetary Transmission Spectra Using Neural Networks

By Keith Cowing

Status Report

astro-ph.EP

February 27, 2026

Filed under astro-ph.EP, autoencoder, exoplanet, JWST, K2-18b, Stellar Cartography, sub-Neptune, Transmission spectroscopy, TRAPPIST-1e

Efficient Reduction Of Stellar Contamination And Noise In Planetary Transmission Spectra Using Neural Networks — Schematic representation of the effect of stellar contamination (TLS) with actual examples of the effect of TLS in simulated signals. In the left column, we show the stellar spectrum, both when the photosphere is clean (no heterogeneities, stellar spots, or faculae) and when the star exhibits heterogeneities (second and third rows). The rows show the difference (residual) between the clean and contaminated stellar spectra. The residuals have been amplified relative to the spectra to highlight regions where the effects are more pronounced. In the right column, we show the corresponding transmission spectra: in the upper half, the clean photosphere case, and in the bottom rows, the contaminated case. In all cases, we have illustrated the simpler case when the chord does not include any heterogeneity, cspot = cfac = 0 (see text). — astro-ph.EP

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 χ2 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.

David S. Duque-Castaño, Lauren Flor-Torres, Jorge I. Zuluaga

Comments: 16 pages, 11 figures. Submitted to Astronomy & Astrophysics. Unabridged version
Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
Cite as: arXiv:2602.10330 [astro-ph.EP] (or arXiv:2602.10330v2 [astro-ph.EP] for this version)
https://doi.org/10.48550/arXiv.2602.10330
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Submission history
From: David S. Duque-Castaño
[v1] Tue, 10 Feb 2026 22:07:18 UTC (7,569 KB)
[v2] Thu, 12 Feb 2026 02:25:40 UTC (7,419 KB)
https://arxiv.org/abs/2602.10330

Astrobiology,

Keith Cowing

Explorers Club Fellow, ex-NASA Space Station Payload manager/space biologist, Away Teams, Journalist, Lapsed climber, Synaesthete, Na’Vi-Jedi-Freman-Buddhist-mix, ASL, Devon Island and Everest Base Camp veteran, (he/him) 🖖🏻

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