Presentation #102.196 in the session Poster Session.
Recently, several extreme precision radial velocity (EPRV) spectrographs have been providing high-resolution and high signal-to-noise spectra with the express purpose of exoplanet discovery and characterization. A significant barrier to this endeavor is the existence of time-variable features in the spectra from both telluric absorption and stellar variability. Traditional methods discard significant portions of data to minimize the effects of telluric contamination, but new data-driven methods may enable the use of a larger fraction of the available data. While there exist methods for modeling out the telluric features (e.g. Bedell et al. 2019) or the stellar variability (e.g. Gilbertson et al. 2020) individually, there is a need for new tools that are capable of modeling them simultaneously. Here we present StellarSpectraObservationFitting.jl (SSOF), a Julia package for creating data-driven linear models (with fast, physically-motivated Gaussian Process priors) for the time-variable spectral features in both the observer and observed frames. SSOF outputs estimates for the radial velocities, template spectra in both the observer and barycentric frames, and scores and basis vectors that quantify the shapes and amplitudes for the temporal variability of time-variable telluric and stellar features, while accounting for the wavelength-dependent instrumental line-spread function. We have demonstrated SSOF’s state-of-the-art RV precision performance on EXPRES and NEID data and discuss how the resulting model can be used to aid in mitigating remaining sources of correlated noise in the radial velocity time series.