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In recent years, dedicated extreme-precision radial velocity (EPRV)spectrographs have produced vast quantities of high-resolution,high-signal-to-noise time-series spectra for bright stars. These data containvaluable information for the dual purposes of planet detection via the measuredRVs and stellar characterization via the co-added spectra. However,considerable data analysis challenges exist in extracting these data productsfrom the observed spectra at the highest possible precision, including theissue of poorly-characterized telluric absorption features and the common useof an assumed stellar spectral template. In both of these examples,precision-limiting reliance on external information can be sidestepped usingthe data directly. Here we propose a data-driven method to simultaneouslyextract precise RVs and infer the underlying stellar and telluric spectra usinga linear model (in the log of flux). The model employs a convex objective andconvex regularization to keep the optimization of the spectral components fast.We implement this method in wobble, an open-source python package which usesTensorFlow in one of its first non-neural-network applications to astronomicaldata. In this work, we demonstrate the performance of wobble on archival HARPSspectra. We recover the canonical exoplanet 51 Pegasi b, detect the secular RVevolution of the M dwarf Barnard's Star, and retrieve the Rossiter-McLaughlineffect for the Hot Jupiter HD 189733b. The method additionally producesextremely high-S/N composite stellar spectra and detailed time-variabletelluric spectra, which we also present here.

Wobble: A Data-driven Analysis Technique for Time-series Stellar Spectra