Multiple Dimensions of LSST Transient Detection: How do we detect things that go bump in the night that we have not yet thought of?

Details Multiple Dimensions of LSST Transient Detection: How do we detect things that go bump in the night that we have not yet thought of? Multiple Dimensions of LSST Transient Detection: How do we detect things that go bump in the night that we have not yet thought of?

Dec 2004

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004aas...20510824v&link_type=abstract

American Astronomical Society Meeting 205, #108.24; Bulletin of the American Astronomical Society, Vol. 36, p.1531

Computer Science

Learning

Scientific paper

A salient challenge for the Large Synoptic Survey Telescope (LSST) is how to recognize important transients, in real time, in a scene full of normal variations. The data stream will simply be too large for efficient transient identification by human analysts. The broad continuum of properties for both extraneous artifacts and interesting transients make them difficult to deal with on a piecemeal basis with hard-wired code. Further, understanding of the time domain is too incomplete to predict confidently the properties of important changes. We examine the potential of modern Machine Learning (ML) techniques for solving this problem. In particular, we discuss the application of ML techniques for automated anomaly detection that can identify transients without an a priori description. Many anomalies will be instrumentation errors; automating their identification will allow prompt action to maintain LSST data quality. But some of the anomalies are likely to be things that go bump in the night that we have not yet thought of.

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