123 citations · 355 across the 9 of their papers we have counts for
3 papers · 1 filter
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…
Optimizing Automated Classification of Periodic Variable Stars in New Synoptic Surveys
James P. Long, Noureddine El Karoui, John A. Rice +2
Efficient and automated classification of periodic variable stars is becoming increasingly important as the scale of astronomical surveys grows. Several recent papers have used met…
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…