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Automated Classification Techniques for Large Spectroscopic Surveys

Connolly, A. J. and Castander, F. and Genovese, C. and Hilton, E. and Merrelli, A. and Moore, A. W. and Nichol, R. C. and Schneider, J. and Snir, Y. and Szalay, A. S. and Szapudi, I. and Wasserman, L. and Yip, C. W. (2001) Automated Classification Techniques for Large Spectroscopic Surveys. In: Mining the Sky. ESO Astrophysics Symposia. Springer , Berlin, pp. 323-330. ISBN 978-3-540-42468-0. https://resolver.caltech.edu/CaltechAUTHORS:20200325-092231624

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Abstract

With the onset of large, systematically selected spectroscopic surveys we have the opportunity to understand the distribution and evolution of galaxies in terms of the mix of their spectral population. In this proceedings we describe a series of statistical techniques, ranging from Karhunen-Lo’eve transform to wavelet transforms, that are being applied to the spectra from the Sloan Digital Sky Survey in order to define a statistically robust and objective spectral classification scheme. The approach we describe for the classification of galaxy, stellar and QSO spectra is at the interface of astrophysics, statistics and computer science. To enable these techniques to be applied and interpreted successfully requires both robust statistical inference together with fast and efficient computer algorithms. Combining these three disciplines we can fully exploit the wealth of physical information present within the SDSS spectra.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/10849171_38DOIArticle
https://rdcu.be/b3eqKPublisherFree ReadCube access
ORCID:
AuthorORCID
Castander, F.0000-0001-7316-4573
Moore, A. W.0000-0003-1715-6338
Additional Information:© 2001 Springer-Verlag Berlin Heidelberg.
Subject Keywords:Star Formation; Stellar Population; Spectral Energy Distribution; Continuum Shape; Spectroscopic Survey
Series Name:ESO Astrophysics Symposia
Record Number:CaltechAUTHORS:20200325-092231624
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200325-092231624
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:102103
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:25 Mar 2020 17:17
Last Modified:25 Mar 2020 17:17

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