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Examination of time series through randomly broken windows

Abstract

The effect of irregular interruption of data collection (the breaking of the window function) on the spectrum determination of a time series is investigated. It is assumed that there is a uniform probability p that each interval of length tau, of the total interval of length T = tau, yields no data. For the linear case (Fourier transform) it is found that the noise to signal ratio has a (one sigma) value less than epsilon if N exceeds p(-1) (1-p) epsilon (-2). For the quadratic case, the same requirement is met by the less restrictive requirement that N exceed p(-1) (1-p) epsilon (-1). It appears that, if four observatories spaced around the Earth were to operate for 25 days, each for six hours a day (N = 100), and if the probability of cloud cover at any site on any day is 20% (p = 0.8), the r.m.s. noise to signal ratio is 0.25% for frequencies displaced from a sharp strong signal by 15 micro Hz. The noise to signal ratio drops off rapidly if the frequency offset exceeds 15 micro Hz

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