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We have shown that data-driven models are effective for inferring physicalattributes of stars (labels; Teff, logg, [M/H]) from spectra, even when thesignal-to-noise ratio is low. Here we explore whether this is possible when thedimensionality of the label space is large (Teff, logg, and 15 abundances: C,N, O, Na, Mg, Al, Si, S, K, Ca, Ti, V, Mn, Fe, Ni) and the model is non-linearin its response to abundance and parameter changes. We adopt ideas fromcompressed sensing to limit overall model complexity while retaining modelfreedom. The model is trained with a set of 12,681 red-giant stars with highsignal-to-noise spectroscopic observations and stellar parameters andabundances taken from the APOGEE Survey. We find that we can successfully trainand use a model with 17 stellar labels. Validation shows that the model does agood job of inferring all 17 labels (typical abundance precision is 0.04 dex),even when we degrade the signal-to-noise by discarding ~50% of the observingtime. The model dependencies make sense: the spectral derivatives with respectto abundances correlate with known atomic lines, and we identify elementsbelonging to atomic lines that were previously unknown. We recover(anti-)correlations in abundance labels for globular cluster stars, consistentwith the literature. However we find the intrinsic spread in globular clusterabundances is 3--4 times smaller than previously reported. We deliver 17 labelswith associated errors for 87,563 red giant stars, as well as open-source codeto extend this work to other spectroscopic surveys.

The Cannon 2: A data-driven model of stellar spectra for detailed chemical abundance analyses