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Neural Networks and the Classification of Active Galactic Nucleus Spectra

Published online by Cambridge University Press:  25 April 2016

Daya M. Rawson ,
Jeremy Bailey  and
Paul J. Francis
Daya M. Rawson
Affiliation:
Mount Stromlo and Siding Springs Observatory, Australian National University, Private Bag, Weston Creek, ACT, 2611, Australiadaya@merlin.anu.edu.au
Jeremy Bailey
Affiliation:
Anglo-Australian Observatory, PO Box 296, Epping, NSW 2121, Australiajab@aaoepp.aao.gov.au
Paul J. Francis
Affiliation:
School of Physics, University of Melbourne, Parkville, Victoria 3052, Australiapfrancis@physics.unimelb.edu.au
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Abstract

The use of artificial neural networks (ANNs) as a classifier of digital spectra is investigated. Using both simulated and real data, it is shown that neural networks can be trained to discriminate between the spectra of different classes of active galactic nucleus (AGN) with realistic sample sizes and signal-to-noise ratios. By working in the Fourier domain, neural nets can classify objects without knowledge of their redshifts.

Keywords

galaxies: nucleigalaxies: quasars: generalgalaxies: Seyfertsmethods: data analysis
Type
Extragalactic
Information
Publications of the Astronomical Society of Australia , Volume 13 , Issue 3 , June 1996 , pp. 207 - 211
Copyright
Copyright © Astronomical Society of Australia 1996

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