311 citations · 1.3k across the 25 of their papers we have counts for
19 papers · 1 filter
The Swan: Data-Driven Inference of Stellar Surface Gravities for Cool Stars from Photometric Light Curves
Maryum Sayeed, Daniel Huber, Adam Wheeler +1
Stellar light curves are well known to encode physical stellar properties. Precise, automated and computationally inexpensive methods to derive physical parameters from light curve…
An unsupervised method for identifying -enriched stars directly from spectra: Li in LAMOST
Adam Wheeler, Melissa Ness, David W. Hogg
Stars with peculiar element abundances are important markers of chemical enrichment mechanisms. We present a simple method, tangent space projection (TSP), for the detection of …
The age distribution of stars in the Milky Way bulge
Tawny Sit, Melissa Ness
The age and chemical characteristics of the Galactic bulge link to the formation and evolutionary history of the Galaxy. Data-driven methods and large surveys enable stellar ages a…
Data-driven derivation of stellar properties from photometric time series data using convolutional neural networks
Kirsten Blancato, Melissa Ness, Daniel Huber +2
Stellar variability is driven by a multitude of internal physical processes that depend on fundamental stellar properties. These properties are our bridge to reconciling stellar ob…
Abundances in the Milky Way across five nucleosynthetic channels from 4 million LAMOST stars
Adam Wheeler, Melissa Ness, Sven Buder +10
Large stellar surveys are revealing the chemodynamical structure of the Galaxy across a vast spatial extent. However, the many millions of low-resolution spectra observed to date a…
SSSpaNG! Stellar Spectra as Sparse, data-driven, Non-Gaussian processes
Stephen M. Feeney, Benjamin D. Wandelt, Melissa K. Ness
Upcoming million-star spectroscopic surveys have the potential to revolutionize our view of the formation and chemical evolution of the Milky Way. Realizing this potential requires…