Neural Networks and the Classification of Active Galactic Nucleus Spectra
Daya M. Rawson, Jeremy Bailey, Paul J. Francis
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.
Create a lesson
Related papers
On binary pulsars and the force of gravity
Davor Palle
Tidal torques. A critical review of some techniques
Michael Efroimsky, James G. Williams
Dynamics of a Spherical Accretion Shock with Neutrino Heating and Alpha-Particle Recombination
Rodrigo Fernández, Christopher Thompson
Asymptotically FRW black holes
J. T. Firouzjaee, Reza Mansouri
Reaction of Accretion Disks to Abrupt Mass Loss During Binary Black Hole Merger
Sean M. O'Neill, M. Coleman Miller, Tamara Bogdanovic et al.
A Gamma-Ray Burst/Pulsar for Cosmic-Ray Positrons with a Dark Matter-like Spectrum
Kunihito Ioka