LSST Astroinformatics And Astrostatistics: Data-oriented Astronomical Research

Details LSST Astroinformatics And Astrostatistics: Data-oriented Astronomical Research LSST Astroinformatics And Astrostatistics: Data-oriented Astronomical Research

Jan 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011aas...21725209b&link_type=abstract

American Astronomical Society, AAS Meeting #217, #252.09; Bulletin of the American Astronomical Society, Vol. 43, 2011

Computer Science

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The LSST Informatics and Statistics Science Collaboration (ISSC) focuses on research and scientific discovery challenges posed by the very large and complex data collection that LSST will generate. Application areas include astroinformatics, machine learning, data mining, astrostatistics, visualization, scientific data semantics, time series analysis, and advanced signal processing. Research problems to be addressed with these methodologies include transient event characterization and classification, rare class discovery, correlation mining, outlier/anomaly/surprise detection, improved estimators (e.g., for photometric redshift or early onset supernova classification), exploration of highly dimensional (multivariate) data catalogs, and more. We present sample science results from these data-oriented approaches to large-data astronomical research. We present results from LSST ISSC team members, including the EB (Eclipsing Binary) Factory, the environmental variations in the fundamental plane of elliptical galaxies, and outlier detection in multivariate catalogs.

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