On a synthetic JWST/Ariel-style spectral database, XGBoost and SVM with per-spectrum normalization and log-abundance targets outperform random forests and other classical regressors for exoplanet atmospheric retrievals.
Title resolution pending
1 Pith paper cite this work, alongside 12 external citations. Polarity classification is still indexing.
1
Pith paper citing it
12
external citations · OpenAlex
citation-role summary
background 1
citation-polarity summary
fields
astro-ph.EP 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Supervised Machine Learning Methods with Uncertainty Quantification for Exoplanet Atmospheric Retrievals from Transmission Spectroscopy
On a synthetic JWST/Ariel-style spectral database, XGBoost and SVM with per-spectrum normalization and log-abundance targets outperform random forests and other classical regressors for exoplanet atmospheric retrievals.