2 papers
astro-ph.EP2020
Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects
B. Henghes, O. Lahav, D. W. Gerdes +56
In this paper we investigate how implementing machine learning could improve the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark Energy Survey (DES) data wh…
astro-ph.IM2018
The Diverse Science Return from a Wide-Area Survey of the Galactic Plane
R. A. Street, M. B. Lund, S. Khakpash +19
The overwhelming majority of objects visible to LSST lie within the Galactic Plane. Though many previous surveys have avoided this region for fear of stellar crowding, LSST's spati…