Neural networks and the classification of active Galactic nucleus spectra

Daya M. Rawson*, Jeremy Bailey, Paul J. Francis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)207-211
Number of pages5
JournalPublications of the Astronomical Society of Australia
Volume13
Issue number3
Publication statusPublished - Oct 1996

Keywords

  • Data analysis
  • Galaxies
  • General - Galaxies
  • Nuclei - Galaxies
  • Quasars
  • Seyferts - Methods

Fingerprint Dive into the research topics of 'Neural networks and the classification of active Galactic nucleus spectra'. Together they form a unique fingerprint.

Cite this