From Data to Knowledge - 111 - Josh Bloom
Channel: ckleinastro
Date: 2012-05-08

Joshua Bloom: "Classification of Astronomical Time Series in the Synoptic Survey Era". A video from the UC Berkeley Conference: From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications (May 7-11, 2012).

Abstract:
Joshua Bloom (UC Berkeley, Astronomy)
We have entered the Synoptic Survey Era of observational astronomy, where data rates are quickly reaching several terabytes per night. A growing army of telescopes monitor, nightly, the brightnesses of millions, and soon to be upwards of a billion objects. Real-time analysis of these data is critical to determine which objects and events require timely observations with expensive follow-up resources. To maximize the scientific returns from these massive projects, sophisticated machine-learning tools must be used. Our group has been on the cutting edge of the methodological and algorithmic development for time-domain astronomical data analysis. I will describe several problems in which we have made great strides, including real-time ML classification of transient events, photometric supernova typing, and probabilistic classification of variable stars from long-baseline time series. We address a multitude of statistical issues, and in this talk I will describe our use of manifold learning for feature extraction in time series, active learning to overcome sample-selection biases, and semi-supervised learning to detect anomalies in data streams. I will describe the newly released Machine-learned ASAS Classification Catalog (MACC, www.bigmacc.info) and discuss the future of astronomical source catalogs.