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The properties of exoplanet host stars are traditionally characterizedthrough a detailed forward-modeling analysis of high-resolution spectra.However, many exoplanet radial velocity surveys employ iodine-cell-calibratedspectrographs, such that the vast majority of spectra obtained include animprinted forest of iodine absorption lines. For surveys that use iodine cells,iodine-free "template" spectra must be separately obtained for precise stellarcharacterization. These template spectra often require extensive additionalobserving time to obtain, and they are not always feasible to obtain for faintstars. In this paper, we demonstrate that machine learning methods can beapplied to infer stellar parameters and chemical abundances fromiodine-imprinted spectra with high accuracy and precision. The methodspresented in this work are broadly applicable to any iodine-cell-calibratedspectrograph. We make publicly available our spectroscopic pipeline, the CannonHIRES Iodine Pipeline (CHIP), which derives stellar parameters and 15 chemicalabundances from iodine-imprinted spectra of FGK stars and which has been set upfor ease of use with Keck/HIRES spectra. Our proof-of-concept offers anefficient new avenue to rapidly estimate a large number of stellar parameterseven in the absence of an iodine-free template spectrum.

Inferring Stellar Parameters from Iodine-Imprinted Keck/HIRES Spectra with Machine Learning