  We present a deep-learning based approach for measuring small planetary
radial velocities in the presence of stellar variability. We use neural
networks to reduce stellar RV jitter in three years of HARPS-N sun-as-a-star
spectra. We develop and compare dimensionality-reduction and data splitting
methods, as well as various neural network architectures including single line
CNNs, an ensemble of single line CNNs, and a multi-line CNN. We inject
planet-like RVs into the spectra and use the network to recover them. We find
that 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. This
approach shows promise for mitigating stellar RV variability and enabling the
detection of small planetary RVs with unprecedented precision.
