1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
Emulating the Global Change Analysis Model with Deep Learning
Andrew Holmes, Matt Jensen, Sarah Coffland +7
The Global Change Analysis Model (GCAM) simulates complex interactions between the coupled Earth and human systems, providing valuable insights into the co-evolution of land, water…
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…