Periodicity analysis of unevenly collected data is a relevant issue in several scienti c elds. In astrophysics, for example, we have to nd the fundamental period of light or radial velocity curves which are unevenly sampled observations of stars. Classical spectral analysis methods are unsatisfactory to solve the problem. In this paper we present a neural network based estimator system which performs well the frequency extraction in unevenly sampled signals. It uses an unsupervised Hebbian nonlinear neural algorithm to extract, from the interpolated signal, the principal components which, in turn, are used by the MUSIC frequency estimator algorithm to extract the frequencies. The neural network is tolerant to noise and works well also with few points in the sequence. We benchmark the system on synthetic and real signals with the Periodogram and with the Cramer-Rao lower bound.

Spectral Analysis of Stellar Light Curves by Means of Neural Networks

CIARAMELLA, Angelo;
1999

Abstract

Periodicity analysis of unevenly collected data is a relevant issue in several scienti c elds. In astrophysics, for example, we have to nd the fundamental period of light or radial velocity curves which are unevenly sampled observations of stars. Classical spectral analysis methods are unsatisfactory to solve the problem. In this paper we present a neural network based estimator system which performs well the frequency extraction in unevenly sampled signals. It uses an unsupervised Hebbian nonlinear neural algorithm to extract, from the interpolated signal, the principal components which, in turn, are used by the MUSIC frequency estimator algorithm to extract the frequencies. The neural network is tolerant to noise and works well also with few points in the sequence. We benchmark the system on synthetic and real signals with the Periodogram and with the Cramer-Rao lower bound.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/25421
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