Modern methods of spectral estimation based on parametric time-series models are useful tools in power spectral analysis. We apply the autoregressive （AR） model to study quasi-periodic oscillations （QPOs）. An empi...Modern methods of spectral estimation based on parametric time-series models are useful tools in power spectral analysis. We apply the autoregressive （AR） model to study quasi-periodic oscillations （QPOs）. An empirical formula to estimate the expectation and standard deviation of the noise AR power densities is derived, which can be used to estimate the statistical significance of an apparent QPO peak in an AR spectrum. An iterative adding-noise algorithm in AR spectral analysis is proposed and applied to studying QPOs in the X-ray binary Cir X-1.展开更多
基金Supported by the National Natural Science Foundation of China and by the State Basic Science Research Projects of China.
文摘Modern methods of spectral estimation based on parametric time-series models are useful tools in power spectral analysis. We apply the autoregressive （AR） model to study quasi-periodic oscillations （QPOs）. An empirical formula to estimate the expectation and standard deviation of the noise AR power densities is derived, which can be used to estimate the statistical significance of an apparent QPO peak in an AR spectrum. An iterative adding-noise algorithm in AR spectral analysis is proposed and applied to studying QPOs in the X-ray binary Cir X-1.
