123 citations · 228 across the 2 of their papers we have counts for
4 papers
Using Machine Learning for Discovery in Synoptic Survey Imaging
Henrik Brink, Joseph W. Richards, Dovi Poznanski +4
Modern time-domain surveys continuously monitor large swaths of the sky to look for astronomical variability. Astrophysical discovery in such data sets is complicated by the fact t…
Construction of a Calibrated Probabilistic Classification Catalog: Application to 50k Variable Sources in the All-Sky Automated Survey
Joseph W. Richards, Dan L. Starr, Adam A. Miller +4
With growing data volumes from synoptic surveys, astronomers must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources,…
Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification
Joseph W. Richards, Dan L. Starr, Henrik Brink +6
Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in l…
Strong Lensing in Abell 1703: Constraints on the Slope of the Inner Dark Matter Distribution
M. Limousin, J. Richard, J. -P. Kneib +9
In this article, we apply strong lensing techniques in Abell 1703, a massive X-ray luminous galaxy cluster at z=0.28. Our analysis is based on imaging data both from space and grou…