LombScarglebased
LombScarglebased refers to methods that utilize the Lomb-Scargle periodogram to detect and characterize periodic signals in unevenly sampled time series. The technique was developed independently by Nicolaas R. Lomb in 1976 and Jeffrey D. Scargle in 1982 and has since become a standard tool in time-domain data analysis. Unlike a classical Fourier periodogram, Lomb-Scargle accommodates irregular sampling without requiring interpolation.
In its standard form, Lomb-Scargle computes a periodogram as a function of frequency by fitting, for each
Variants include Generalized Lomb-Scargle (GLS) which allows a floating mean and heteroscedastic errors, the floating-mean Lomb-Scargle,
Applications include detection of periodic signals in astronomical time series: variable stars, pulsations, and radial velocity
Software implementations are widely available in scientific libraries, such as Python's Astropy and SciPy, R's lomb