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Activity Number: 225
Type: Topic Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #303993
Title: Robust Event Detection for Massive Time-Series Surveys
Author(s): Alexander Blocker*+
Companies: Harvard University
Address: 852 Massachusetts Ave, Cambridge, MA, 02139, United States
Keywords: time series ; event detection ; massive data ; astrostatistics ; microlensing ; robust
Abstract:

The detection and analysis of events within massive collections of time-series has become an extremely important task for time-domain astronomy. In particular, many scientific investigations (e.g. the analysis of microlensing and other transients) begin with the detection of isolated events in irregularly-sampled time series with non-linear trends and non-Gaussian noise. We outline a semi-parametric, robust, parallel method for identifying variability and isolated events at multiple scales in the presence of the above complications. This approach harnesses the power of Bayesian modeling while maintaining much of the speed and scalability of more ad-hoc approaches. We contrast this work with event detection methods from other fields, highlighting the unique challenges posed by astronomical surveys. Finally, we present results from the application of this method to 87.2 million sources from the EROS-2 survey, where we have obtained a greater than 100-fold reduction in candidates for important classes of phenomena and created high-quality summaries for subsequent analyses.


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