Astron. Astrophys. Suppl. Ser. 137, 391-405
R. Tagliaferri 1 - A. Ciaramella 2 - L. Milano 3 - F. Barone 3 - G. Longo 4
Send offprint request: R. Tagliaferri, Dipartimento di Matematica ed Informatica, Università di Salerno, via S. Allende, 84081 Baronissi (SA), Italy.
1 - Dipartimento di Matematica ed Informatica, Università di Salerno, via S. Allende, 84081 Baronissi (SA) Italia and INFM, unità di Salerno, 84081 Baronissi (SA), Italy
2 - Università di Salerno, 84081 Baronissi (SA), Italy
3 - Dipartimento di Scienze Fisiche, Università di Napoli "Federico II", Italy, and Istituto Nazionale di Fisica Nucleare, sez. Napoli, Complesso Universitario di Monte Sant'Angelo, via Cintia, 80126 Napoli, Italy
4 - Osservatorio Astronomico di Capodimonte, via Moiariello 16, 80131 Napoli, Italy
Received December 2, 1997; accepted March 25, 1999
Periodicity analysis of unevenly collected data is a relevant issue in several scientific fields. In astrophysics, for example, we have to find 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.
Key words: methods: data analysis -- techniques: radial velocities -- stars: binaries: eclipsing
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