Presentation #401.08 in the session Exoplanet Radial Velocities — iPoster Session.
Stellar variability is one of the largest contributors to noise in Extreme Precision Radial Velocity (EPRV) measurements. We are developing deep learning-based approaches to measure small injected planet-like RVs in the presence of larger amplitude RV noise caused by stellar activity. Our networks are trained using the HARPS-N sun-as-a-star extracted (order-by-order) spectra from 2015-2018, with the goal of using NEID sun-as-a-star spectra in the near future. The unprecedented signal-to-noise and cadence of sun-as-a-star spectra allow us to evaluate the effectiveness and limitations of neural networks at separating stellar and planet-induced RVs in the wavelength domain at sub-m/s precision, and determine their applicability to the EPRV community’s goal of mitigating stellar RV variability.