AI Insight
This study presents non-local thermodynamic equilibrium (NLTE) abundance corrections for 360,000 red giant stars observed by the SDSS-V Milky Way Mapper survey using infrared spectroscopy. The researchers developed neural network emulators to efficiently fit stellar spectra and calculate more accurate chemical abundances for eight elements, finding significant NLTE effects of approximately 0.1 dex for aluminum, manganese, and titanium. The work addresses systematic errors that arise when the common LTE assumption breaks down for luminous stars like red giants.
Why it matters
These corrections improve the accuracy of chemical abundance measurements for hundreds of thousands of stars, which is crucial for understanding the chemical evolution and formation history of the Milky Way galaxy. More accurate stellar abundances enable better constraints on galactic archaeology studies and models of stellar nucleosynthesis.
Understand the Science
arXiv:2607.22499v1 Announce Type: new
Abstract: The majority of spectroscopic surveys assume local thermodynamic equilibrium (LTE) during the modeling of stellar spectra. This assumption begins to break down for luminous stars, like the red giants targeted by SDSS-V’s Milky Way Mapper Survey in its Galactic Genesis program. In this work, we present non-LTE (NLTE) abundances for 360,000 red giant stars in Milky Way Mapper DR19, from infrared APOGEE spectra. We generate NLTE spectra using precomputed departure coefficient grids for Na, Mg, Si, Al, Ca, Ti, Mn, and Ni. To fit APOGEE spectra at scale, we train neural network emulators (NNEs) to synthesize LTE and NLTE H-band spectra. After verifying that the NNEs are accurate, we fit the APOGEE spectra with ASPCAP results that fall within the same parameter range as the training data. We find strong NLTE effects on the order of 0.1,dex for Al, Mn, and Ti, and smaller effects for Si and Ni. We provide a catalog of the results of our LTE and NLTE fits, as well as NLTE-corrected ASPCAP abundances using a polynomial fit correction.