@article{2012PASP..124.1175B,
 abstract = {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 loop. Here we
present the inner workings of a framework, based on machine-
learning algorithms, that captures expert training and ground-
truth knowledge about the variable and transient sky to automate
(1) the process of discovery on image differences, and (2) the
generation of preliminary science-type classifications of
discovered sources. Since follow-up resources for extracting
novel science from fast-changing transients are precious, self-
calibrating classification probabilities must be couched in
terms of efficiencies for discovery and purity of the samples
generated. We estimate the purity and efficiency in identifying
real sources with a two-epoch image-difference discovery
algorithm for the Palomar Transient Factory (PTF) survey. Once
given a source discovery, using machine-learned classification
trained on PTF data, we distinguish between transients and
variable stars with a 3.8% overall error rate (with 1.7%
errors for imaging within the Sloan Digital Sky Survey
footprint). At &gt;96% classification efficiency, the samples
achieve 90% purity. Initial classifications are shown to rely
primarily on context-based features, determined from the data
itself and external archival databases. In the first year of
autonomous operations of PTF, this discovery and classification
framework led to several significant science results, from
outbursting young stars to subluminous Type IIP supernovae to
candidate tidal disruption events. We discuss future directions
of this approach, including the possible roles of crowdsourcing
and the scalability of machine learning to future surveys such
as the Large Synoptic Survey Telescope (LSST).},
 adsnote = {Provided by the SAO/NASA Astrophysics Data System},
 adsurl = {https://ui.adsabs.harvard.edu/abs/2012PASP..124.1175B},
 archiveprefix = {arXiv},
 author = {<NAME>. and <NAME>. and <NAME>. and
<NAME>. and <NAME>. and <NAME>. and
<NAME>. and <NAME>. and <NAME>. and
<NAME>. and <NAME>. and <NAME>. and <NAME>.},
 doi = {10.1086/668468},
 eprint = {1106.5491},
 journal = {\pasp},
 keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies},
 month = {Nov},
 number = {921},
 pages = {1175},
 primaryclass = {astro-ph.IM},
 title = {Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era},
 volume = {124},
 year = {2012}
}

