48 citations · 107 across the 7 of their papers we have counts for
4 papers · 1 filter
Probabilistic neural network approach to determining parameters of eclipsing binaries
Marina Kounkel, Logan Sizemore, Hidemi Mitani Shen +7
Eclipsing binaries provide one of the most direct mechanisms for measuring stellar properties such as mass and radius, but historically, determining these properties has been non-t…
Gaia Net: Towards robust spectroscopic parameters of stars of all evolutionary stages
Dylan Huson, Indiana Cowan, Logan Sizemore +2
We present a new processing of XP spectra for 220 million stars released in Gaia DR3. The new data model is capable of handling objects with Teff between 2000 and 50,000 K, and wit…
A self-consistent data-driven model for determining stellar parameters from optical and near-IR spectra
Logan Sizemore, Diego Llanes, Marina Kounkel +3
Data-driven models, which apply machine learning to infer physical properties from large quantities of data, have become increasingly important for extracting stellar properties fr…
APOGEE Net: Improving the derived spectral parameters for young stars through deep learning
Richard Olney, Marina Kounkel, Chad Schillinger +6
Machine learning allows efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of ML approaches has been demonstra…