Real-time fundamental frequency estimation by least-square fitting

For real-time applications, a fundamental frequency estimation algorithm must be able to obtain accurate estimates from short signal segments. Characterization of the error function of fitting a sinusoid to the signal segment allows its spectrum to be deduced and the algorithm to be implemented efficiently. Musical signals are discussed in particular.published_or_final_versio

Millisecond and Binary Pulsars as Nature's Frequency Standards. II. Effects of Low-Frequency Timing Noise on Residuals and Measured Parameters

Pulsars are the most stable natural frequency standards. They can be applied to a number of principal problems of modern astronomy and time-keeping metrology. The full exploration of pulsar properties requires obtaining unbiased estimates of the spin and orbital parameters. These estimates depend essentially on the random noise component being revealed in the residuals of time of arrivals (TOA). In the present paper, the influence of low-frequency ("red") timing noise with spectral indices from 1 to 6 on TOA residuals, variances, and covariances of estimates of measured parameters of single and binary pulsars are studied. In order to determine their functional dependence on time, an analytic technique of processing of observational data in time domain is developed which takes into account both stationary and non-stationary components of noise. Our analysis includes a simplified timing model of a binary pulsar in a circular orbit and procedure of estimation of pulsar parameters and residuals under the influence of red noise. We reconfirm that uncorrelated white noise of errors of measurements of TOA brings on gradually decreasing residuals, variances and covariances of all parameters. On the other hand, we show that any red noise causes the residuals, variances, and covariances of certain parameters to increase with time. Hence, the low frequency noise corrupts our observations and reduces experimental possibilities for better tests of General Relativity Theory. We also treat in detail the influence of a polynomial drift of noise on the residuals and fitting parameters. Results of the analitic analysis are used for discussion of a statistic describing stabilities of kinematic and dynamic pulsar time scales.Comment: 40 pages, 1 postscript figure, 1 picture, uses mn.sty, accepted to Mon. Not. Roy. Astron. So

Determining global parameters of the oscillations of solar-like stars

Helioseismology has enabled us to better understand the solar interior, while also allowing us to better constrain solar models. But now is a tremendous epoch for asteroseismology as space missions dedicated to studying stellar oscillations have been launched within the last years (MOST and CoRoT). CoRoT has already proved valuable results for many types of stars, while Kepler, which was launched in March 2009, will provide us with a huge number of seismic data very soon. This is an opportunity to better constrain stellar models and to finally understand stellar structure and evolution. The goal of this research work is to estimate the global parameters of any solar-like oscillating target in an automatic manner. We want to determine the global parameters of the acoustic modes (large separation, range of excited pressure modes, maximum amplitude, and its corresponding frequency), retrieve the surface rotation period of the star and use these results to estimate the global parameters of the star (radius and mass).To prepare the analysis of hundreds of solar-like oscillating stars, we have developed a robust and automatic pipeline. The pipeline consists of data analysis techniques, such as Fast Fourier Transform, wavelets, autocorrelation, as well as the application of minimisation algorithms for stellar-modelling. We apply our pipeline to some simulated lightcurves from the asteroFLAG team and the Aarhus-asteroFLAG simulator, and obtain results that are consistent with the input data to the simulations. Our strategy gives correct results for stars with magnitudes below 11 with only a few 10% of bad determinations among the reliable results. We then apply the pipeline to the Sun and three CoRoT targets.In particular we determine the parameters of the Sun, HD49933, HD181906, and HD181420.Comment: 15 pages, 17 figures, accepted for publication in A&

On line power spectra identification and whitening for the noise in interferometric gravitational wave detectors

In this paper we address both to the problem of identifying the noise Power Spectral Density of interferometric detectors by parametric techniques and to the problem of the whitening procedure of the sequence of data. We will concentrate the study on a Power Spectral Density like the one of the Italian-French detector VIRGO and we show that with a reasonable finite number of parameters we succeed in modeling a spectrum like the theoretical one of VIRGO, reproducing all its features. We propose also the use of adaptive techniques to identify and to whiten on line the data of interferometric detectors. We analyze the behavior of the adaptive techniques in the field of stochastic gradient and in the Least Squares ones.Comment: 28 pages, 21 figures, uses iopart.cls accepted for pubblication on Classical and Quantum Gravit

On the Spectral Properties of Matrices Associated with Trend Filters

This paper is concerned with the spectral properties of matrices associated with linear filters for the estimation of the underlying trend of a time series. The interest lies in the fact that the eigenvectors can be interpreted as the latent components of any time series that the filter smooths through the corresponding eigenvalues. A difficulty arises because matrices associated with trend filters are finite approximations of Toeplitz operators and therefore very little is known about their eigenstructure, which also depends on the boundary conditions or, equivalently, on the filters for trend estimation at the end of the sample. Assuming reflecting boundary conditions, we derive a time series decomposition in terms of periodic latent components and corresponding smoothing eigenvalues. This decomposition depends on the local polynomial regression estimator chosen for the interior. Otherwise, the eigenvalue distribution is derived with an approximation measured by the size of the perturbation that different boundary conditions apport to the eigenvalues of matrices belonging to algebras with known spectral properties, such as the Circulant or the Cosine. The analytical form of the eigenvectors is then derived with an approximation that involves the extremes only. A further topic investigated in the paper concerns a strategy for a filter design in the time domain. Based on cut-off eigenvalues, new estimators are derived, that are less variable and almost equally biased as the original estimator, based on all the eigenvalues. Empirical examples illustrate the effectiveness of the method