Towards the Automated Classification of Variable Objects and Transients

Details Towards the Automated Classification of Variable Objects and Transients Towards the Automated Classification of Variable Objects and Transients

Jan 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009aas...21342706m&link_type=abstract

American Astronomical Society, AAS Meeting #213, #427.06; Bulletin of the American Astronomical Society, Vol. 41, p.258

Computer Science

Learning

Scientific paper

With the advent of the next generation of synoptic sky surveys, leading towards LSST, the expected numbers of highly variable or transient sources will overwhelm our capabilities for follow-up observations. This is a critical bottleneck for the otherwise highly promising science from such surveys. In order to deploy the scarce follow-up resources in an optimal fashion, detected events must be classified and prioritized, depending on the scientific context. We tackle this challenge on the example of event data streams from the Palomar-Quest survey (http://palquest.org), and Catalina Sky Survey (http://www.lpl.arizona.edu/css). The events are published and distributed in real time using the VOEventNet system (http://voeventnet.caltech.edu). These survey data are supplemented with data from other telescope networks and archives, including GCVS, AAVSO, CVNet, VSNet, DPOSS, SDSS, etc. In addition to various source parameters (e.g., magnitudes, colors, etc.), we also consider contextual information (e.g., Galactic or ecliptic latitude, presence of a possible host galaxy nearby, etc.). We use a dual approach to transient event classification, Bayesian techniques (including Naive Bayes Classifier, and Bayesian Networks) and machine learning techniques (e.g., Artificial Neural Nets, Support Vector Machines, etc.). In the former approach, we use archival data to generate prior distributions for various observable parameters such as flux changes over a particular time baseline, for large samples of known classes and subclasses of astrophysical variables or transient objects and events, ranging from pulsating variables to blazars and supernovae. We also obtain colors and light-curves for some of the recently active objects at Palomar 60-inch telescope. These data are folded in the analysis, for a dynamically evolving set of classifications.

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