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We present a deep-learning based approach for measuring small planetaryradial velocities in the presence of stellar variability. We use neuralnetworks to reduce stellar RV jitter in three years of HARPS-N sun-as-a-starspectra. We develop and compare dimensionality-reduction and data splittingmethods, as well as various neural network architectures including single lineCNNs, an ensemble of single line CNNs, and a multi-line CNN. We injectplanet-like RVs into the spectra and use the network to recover them. We findthat the multi-line CNN is able to recover planets with 0.2 m/s semi-amplitude,50 day period, with 8.8% error in the amplitude and 0.7% in the period. Thisapproach shows promise for mitigating stellar RV variability and enabling thedetection of small planetary RVs with unprecedented precision.

Deep-learning based measurement of planetary radial velocities in the presence of stellar variability