Neural Networks and the Classification of Active Galactic Nucleus Spectra
Astrophysics
2021-10-13 v1
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
Cite
@article{arxiv.astro-ph/9607148,
title = {Neural Networks and the Classification of Active Galactic Nucleus Spectra},
author = {Daya M. Rawson and Jeremy Bailey and Paul J. Francis},
journal= {arXiv preprint arXiv:astro-ph/9607148},
year = {2021}
}
Comments
11 pages, LaTeX, including two postscript figures, 41 kb. Accepted for publication in Publ. ASA