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Published December 5, 2008 | Published
Book Section - Chapter Open

Towards real-time classification of astronomical transients


Exploration of time domain is now a vibrant area of research in astronomy, driven by the advent of digital synoptic sky surveys. While panoramic surveys can detect variable or transient events, typically some follow-up observations are needed; for short-lived phenomena, a rapid response is essential. Ability to automatically classify and prioritize transient events for follow-up studies becomes critical as the data rates increase. We have been developing such methods using the data streams from the Palomar-Quest survey, the Catalina Sky Survey and others, using the VOEventNet framework. The goal is to automatically classify transient events, using the new measurements, combined with archival data (previous and multi-wavelength measurements), and contextual information (e.g., Galactic or ecliptic latitude, presence of a possible host galaxy nearby, etc.); and to iterate them dynamically as the follow-up data come in (e.g., light curves or colors). We have been investigating Bayesian methodologies for classification, as well as discriminated follow-up to optimize the use of available resources, including Naive Bayesian approach, and the non-parametric Gaussian process regression. We will also be deploying variants of the traditional machine learning techniques such as Neural Nets and Support Vector Machines on datasets of reliably classified transients as they build up.

Additional Information

© 2008 American Institute of Physics. Issue Date: 5 December 2008. We are grateful to the staff of Palomar Observatory for their help, and to our collaborators in PQ and CSS survey teams. This work was supported in part by the NSF grants AST-0407448, AST-0326524, and CNS-0540369, and by the Ajax Foundation. A.K. and B.H. were supported in part by the Caltech SURF program.

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