315 citations · 3k across the 32 of their papers we have counts for
4 papers · 1 filter
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,…
Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era
J. S. Bloom, J. W. Richards, P. E. Nugent +10
The rate of image acquisition in modern synoptic imaging surveys has already begun to outpace the feasibility of keeping astronomers in the real-time discovery and classification l…
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…
On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
Joseph W. Richards, Dan L. Starr, Nathaniel R. Butler +6
With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for new…