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.
|Number of pages||5|
|Journal||Publications of the Astronomical Society of Australia|
|Publication status||Published - Oct 1996|
- Data analysis
- General - Galaxies
- Nuclei - Galaxies
- Seyferts - Methods