We investigate the potential of neural-network based classifiers for
discriminating gravitational wave bursts (GWBs) of a given canonical family
(e.g. core-collapse supernova waveforms) from typical transient instrumental
artifacts (glitches), in the data of a single detector. The further
classification of glitches into typical sets is explored.In order to provide a
proof of concept,we use the core-collapse supernova waveform catalog produced
by H. Dimmelmeier and co-Workers, and the data base of glitches observed in
laser interferometer gravitational wave observatory (LIGO) data maintained by
P. Saulson and co-Workers to construct datasets of (windowed) transient
waveforms (glitches and bursts) in additive (Gaussian and compound-Gaussian)
noise with different signal-tonoise ratios (SNR). Principal component analysis
(PCA) is next implemented for reducing data dimensionality, yielding results
consistent with, and extending those in the literature. Then, a multilayer
perceptron is trained by a backpropagation algorithm (MLP-BP) on a data subset,
and used to classify the transients as glitch or burst. A Self-Organizing Map
(SOM) architecture is finally used to classify the glitches. The glitch/burst
discrimination and glitch classification abilities are gauged in terms of the
related truth tables. Preliminary results suggest that the approach is
effective and robust throughout the SNR range of practical interest.
Perspective applications pertain both to distributed (network, multisensor)
detection of GWBs, where someintelligenceat the single node level can be
introduced, and instrument diagnostics/optimization, where spurious transients
can be identified, classified and hopefully traced back to their entry point
