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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
years
2026 2representative citing papers
Interpolation algorithm choice in template-based RV extraction from high-resolution spectra introduces systematic biases reaching 20-25 m/s in low-SNR cases and under 0.2 m/s when BERV variation is large, demonstrated via Gaussian synthetic spectra and ESPRESSO observations.
citing papers explorer
-
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.
-
The impact of interpolation in high-resolution spectroscopy -- The overlooked role of interpolation in radial velocity extraction
Interpolation algorithm choice in template-based RV extraction from high-resolution spectra introduces systematic biases reaching 20-25 m/s in low-SNR cases and under 0.2 m/s when BERV variation is large, demonstrated via Gaussian synthetic spectra and ESPRESSO observations.