EDP Sciences
Free access
Volume 435, Number 2, May IV 2005
Page(s) 773 - 780
Section Instruments, observational techniques, and data processing
DOI http://dx.doi.org/10.1051/0004-6361:20042154

A&A 435, 773-780 (2005)
DOI: 10.1051/0004-6361:20042154

Time series analysis in astronomy: Limits and potentialities

R. Vio1, N. R. Kristensen2, H. Madsen2 and W. Wamsteker3

1  Chip Computers Consulting s.r.l., Viale Don L. Sturzo 82, S.Liberale di Marcon, 30020 Venice, Italy ESA-VILSPA, Apartado 50727, 28080 Madrid, Spain
    e-mail: robertovio@tin.it
2  Department of Informatics and Mathematical Modelling, Technical University of Denmark, Richard Petersens Plads, 2800 Kgs. Lyngby, Denmark
    e-mail: nrk@imm.dtu.dk; hm@imm.dtu.dk

3  ESA-VILSPA, Apartado 50727, 28080 Madrid, Spain
    e-mail: willem.wamsteker@esa.int

(Received 11 October 2004 / Accepted 19 January 2005 )

In this paper we consider the problem of the limits concerning the physical information that can be extracted from the analysis of one or more time series (light curves) typical of astrophysical objects. On the basis of theoretical considerations and numerical simulations, we show that with no a priori physical model there are not many possibilities to obtain interpretable results. For this reason, the practice to develop more and more sophisticated statistical methods of time series analysis is not productive. Only techniques of data analysis developed in a specific physical context can be expected to provide useful results. The field of stochastic dynamics appears to be an interesting framework for such an approach. In particular, it is shown that modelling the experimental time series by means of the stochastic differential equations (SDE) represents a valuable tool of analysis. For example, besides a more direct connection between data analysis and theoretical models, in principle the use of SDE permits the analysis of a continuous signal independent of the characteristics (e.g., frequency, regularity, ...) of the sampling with which the experimental time series were obtained. In this respect, an efficient approach based on the extended Kalman filter technique is presented. Its performances and limits are discussed and tested through numerical experiments. Freely downloadable software is made available.

Key words: methods: data analysis -- methods: statistical

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