This week I published the preprint of a manuscript that started as a blog post, but quickly out-grew this medium: Understanding the Lomb-Scargle Periodogram.
Over the last couple years I've written a number of Python implementations of the Lomb-Scargle periodogram (I'd recommend AstroPy's
LombScargle in most cases today), and also wrote a marginally popular blog post and somewhat pedagogical paper on the subject.
This all has led to a steady trickle of emails from students and researchers asking for advice on applying and interpreting the Lomb-Scargle algorithm, particularly for astronomical data.
I noticed that these queries tended to repeat many of the same questions and express some similar misconceptions, and this paper is my attempt to address those once and for all — in a "mere" 55 pages (which includes 26 figures and 4 full pages of references, so it's not all that bad).