2026ApJ...997..238A

Data-driven stellar classification has a long and important history in astronomy, dating as far back as Annie Jump Cannon's “by-eye” classifications of stars into spectral types, still used today. In recent years, data-driven spectroscopy has proven to be an effective means of deriving stellar properties for large samples of stars, sidestepping issues with computational efficiency, incomplete line lists, and radiative transfer calculations associated with physical stellar models. A logical application of these algorithms is the detection of unresolved stellar binaries, which requires accurate spectroscopic models to resolve flux contributions from a fainter secondary star in the spectrum. Here, we use The Cannon to train a data-driven model on spectra from the Keck High Resolution Echelle Spectrometer. We show that our model is competitive with existing data-driven models in its ability to predict stellar properties T_eff, R_⋆, [Fe/H], and $v\,{\rm{\sin }}\,i$, as well as the instrumental point-spread function, particularly when we apply a novel wavelet-based processing step to spectra before training. We find that even with accurate estimates of star properties, our model's ability to detect unresolved binaries is limited by its ∼3% accuracy in per-pixel flux predictions, illuminating possible limitations of data-driven model applications.