Deep neural networks using temperature-based spectral representations recover planetary Doppler signals with amplitudes of at least 25 cm/s from HARPS-N solar spectra under cross-validation.
and Hogg, David W
2 Pith papers cite this work, alongside 14 external citations. Polarity classification is still indexing.
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astro-ph.IM 2years
2026 2representative citing papers
A robust, heteroskedastic matrix factorization method generalizes PCA to handle per-feature uncertainties, missing data, and outlier detection via Student-t likelihood iterative reweighting.
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Modeling Doppler Shifts in Radial-Velocity Data with Deep Learning toward Earth-mass Exoplanet Detection
Deep neural networks using temperature-based spectral representations recover planetary Doppler signals with amplitudes of at least 25 cm/s from HARPS-N solar spectra under cross-validation.
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Robust Heteroskedastic Matrix Factorization: A Generalization of PCA that Flags Outliers and Handles Missing Data
A robust, heteroskedastic matrix factorization method generalizes PCA to handle per-feature uncertainties, missing data, and outlier detection via Student-t likelihood iterative reweighting.