Presentation #102.73 in the session Poster Session.
The radial velocity detection of exoplanets is complicated by stellar spectroscopic variability and instrumental instability, as they distort the spectral line profiles and can be misinterpreted as apparent radial velocity shifts mimicking exoplanets.
We present an improved FourIEr phase SpecTrum Analysis (FIESTA a.k.a. ΦESTA) to disentangle apparent velocity shifts due to a line deformation from a true Doppler shift. ΦESTA projects stellar spectrum’s cross correlation function (CCF) onto a truncated set of Fourier basis functions. Using the amplitude and phase information from each ΦESTA mode, we can trace the line variability at different CCF width scales to robustly identify and mitigate multiple sources of RV contamination.
We find strong correlations between ΦESTA metrics with apparent velocities induced by sunspots in the SOAP solar simulations. In addition, we demonstrate ΦESTA is capable of identifying multiple sources of the spurious solar RV variation in the 3 years of HARPS-N solar observations, including solar rotation, the long-term trend due to the solar magnetic cycle, instrumental instability and apparent solar rotation rate changes. We then employ machine learning techniques to model the solar RV variation using inputs from ΦESTA. In the end, we present the solar variability analysis using ΦESTA on the latest NEID solar observations.