48 citations · 107 across the 7 of their papers we have counts for
7 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…
PRIMRose: Insights into the Per-Residue Energy Metrics of Proteins with Double InDel Mutations using Deep Learning
Stella Brown, Nicolas Preisig, Autumn Davis +2
Understanding how protein mutations affect protein structure is essential for advancements in computational biology and bioinformatics. We introduce PRIMRose, a novel approach that…
The Nineteenth Data Release of the Sloan Digital Sky Survey
SDSS Collaboration, Gautham Adamane Pallathadka, Mojgan Aghakhanloo +210
Mapping the local and distant Universe is key to our understanding of it. For decades, the Sloan Digital Sky Survey (SDSS) has made a concerted effort to map millions of celestial…
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…
RoseNet: Predicting Energy Metrics of Double InDel Mutants Using Deep Learning
Sarah Coffland, Katie Christensen, Filip Jagodzinski +1
An amino acid insertion or deletion, or InDel, can have profound and varying functional impacts on a protein's structure. InDel mutations in the transmembrane conductor regulator p…
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…