Understanding the Lomb–Scargle Periodogram

By J. Vanderplas. 2017

912 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Statistical and Nonlinear Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/409e0e7aaa1480bf01665d1d36b8e6d54b422011

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Daniel Foreman-Mackey
2. Sivaram Ambikasaran
3. E. Agol
4. R. Angus

In Statistical and Nonlinear Physics

1. R. Angus
2. Daniel Foreman-Mackey
3. Sivaram Ambikasaran
4. E. Agol
5. Timothy Morton

In Signal Processing

1. Daniel Foreman-Mackey
2. Sivaram Ambikasaran
3. E. Agol
4. R. Angus

Citations in the corpus

• Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series (2017, methodology): “The top two panels of Figure 10 show the Lomb–Scargle periodogram (VanderPlas 2017) estimator for the power spectrum based on the full 4yr of Kepler data and the month-long subset used in our analysis.”