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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…
The Palomar Transient Factory photometric catalog 1.0
E. O. Ofek, R. Laher, J. Surace +22
We construct a photometrically calibrated catalog of non-variable sources from the Palomar Transient Factory (PTF) observations. The first version of this catalog presented here, 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,…
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…
A Bayesian Approach to Calibrating Period-Luminosity Relations of RR Lyrae Stars in the Mid-Infrared
Christopher R. Klein, Joseph W. Richards, Nathaniel R. Butler +1
A Bayesian approach to calibrating period-luminosity (PL) relations has substantial benefits over generic least-squares fits. In particular, the Bayesian approach takes into accoun…