article Open AccessTop 1% cited
Understanding the Lomb–Scargle Periodogram
The Astrophysical Journal Supplement Series · 2018 · Vol. 236(1) · pp. 16–16
Jake Vanderplas✉(University of Washington)
Abstract
Abstract The Lomb–Scargle periodogram is a well-known algorithm for detecting and characterizing periodic signals in unevenly sampled data. This paper presents a conceptual introduction to the Lomb–Scargle periodogram and important practical considerations for its use. Rather than a rigorous mathematical treatment, the goal of this paper is to build intuition about what assumptions are implicit in the use of the Lomb–Scargle periodogram and related estimators of periodicity, so as to motivate important practical considerations required in its proper application and interpretation.
Fractal and DNA sequence analysisChaos control and synchronizationBlind Source Separation TechniquesPeriodogramEstimatorEconometricsComputer scienceAlgorithmEconomicsMathematicsStatistics
Citations
1,164
FWCI
32.95
field-weighted impact
References
97
Percentile
100%
vs. same field & year
Citations per year
Cited by
Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series
The Astronomical Journal · 2017 · 946 citations
References
Time Series Analysis with Clean - Part One - Derivation of a Spectrum
The Astronomical Journal · 1987 · 798 citations
Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data
The Astrophysical Journal · 1982 · 7,076 citations
Astropy: A community Python package for astronomy
Astronomy and Astrophysics · 2013 · 13,763 citations
The Statistical Analysis of Time Series.
Biometrics · 1995 · 2,044 citations
On characterizing the variability properties of X-ray light curves from active galaxies
Monthly Notices of the Royal Astronomical Society · 2003 · 1,152 citations
The generalised Lomb-Scargle periodogram
Astronomy and Astrophysics · 2009 · 1,632 citations
Citation Network
How this paper connects to the literature. Drag to explore, click any node to open that paper.
