Lightcurve Based Classification Of Transients Events

Details Lightcurve Based Classification Of Transients Events Lightcurve Based Classification Of Transients Events

Jan 2012

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012aas...21944616d&link_type=abstract

American Astronomical Society, AAS Meeting #219, #446.16

Computer Science

Learning

Scientific paper

In many scientific fields, a new generation of instruments is generating exponentially growing data streams, that may enable significant new discoveries. The requirement to perform the analysis rapidly and objectively, coupled with the huge amount of data available, implies a need for an automated event detection, classification, and decision making. In astronomy, this is the case with the new generation of synoptic sky surveys, that discover an ever increasing number of transient events. However, not all of them are equally interesting and worthy of a follow-up with limited resources. This presents some unusual classification challenges: the data are sparse, heterogeneous and incomplete; evolving in time; and most of the relevant information comes from a variety of archival data and contextual information. We are exploring a variety of machine learning techniques, using the ongoing CRTS sky survey as a testbed: Bayesian Network, [dm,dt] histograms, Decision Trees, Neural Networks, Symbolic Regression. In this work we focus on the lightcurve based classification using an hierarchical approach where some astrophysically motivated major features are used to separate different groups of classes. Proceeding down the classification hierarchy every node uses those classifiers that are demonstrated to work best for that particular task.

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