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Unevenly-sampled signals: a general formalism of the Lomb-Scargle periodogram

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arxiv 1006.2473 v1 pith:NID64OO5 submitted 2010-06-12 astro-ph.CO math-phmath.MPphysics.data-an

R. Vio (Chips Computer Consulting , Venice , Italy) , P. Andreani (ESO , INAF-OAT) , A. Biggs (ESO) This is my paper · ORCID
classification astro-ph.COmath-phmath.MPphysics.data-an
keywords periodogramnoisewhencasecharacteristicsformalismgenerallomb-scargle
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The periodogram is a popular tool that tests whether a signal consists only of noise or if it also includes other components. The main issue of this method is to define a critical detection threshold that allows identification of a component other than noise, when a peak in the periodogram exceeds it. In the case of signals sampled on a regular time grid, determination of such a threshold is relatively simple. When the sampling is uneven, however, things are more complicated. The most popular solution in this case is to use the "Lomb-Scargle" periodogram, but this method can be used only when the noise is the realization of a zero-mean, white (i.e. flat-spectrum) random process. In this paper, we present a general formalism based on matrix algebra, which permits analysis of the statistical properties of a periodogram independently of the characteristics of noise (e.g. colored and/or non-stationary), as well as the characteristics of sampling.

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  1. Cataclysmic Variables Photometric Periods from TESS

    astro-ph.SR 2026-07 conditional novelty 6.5 of 10

    TESS photometry yields coherent periods for 1,362 CVs, including roughly 465–565 first determinations, and confirms the 2–3 h period gap with a median period near 3.68 h.

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