lomb-scargle-periodogram

To detect periodic signals in unevenly sampled time series, you compute a Lomb-Scargle periodogram using Python with lightkurve. This method identifies significant periodic components in irregularly sampled observations like stellar light curves, radial velocity, or eclipsing binary data.

A Lomb-Scargle periodogram is the standard tool used for finding periods in unevenly sampled astronomical light curves. It identifies significant periodic signals to detect stellar rotation, pulsations, or eclipsing binaries in time series data from missions like Kepler, K2, and TESS.

You find the strongest period in an irregular time series by inputting your time and flux arrays into a periodogram computation. Using Python with lightkurve and numpy, you can plot the periodogram and extract the peak value to model the detected period.

Yes, you can use lightkurve to analyze unevenly sampled TESS or Kepler data. The library computes Lomb-Scargle periodograms specifically to handle uneven sampling in space mission light curves, allowing you to extract and model the strongest detected periods.

Yes, you need numpy and matplotlib alongside lightkurve to compute, plot, and model the periodogram for stellar rotation detection. These Python dependencies are required to visualize the periodogram and interpret the periodic signals in your time series data.

You use a Lomb-Scargle periodogram instead of a standard Fourier transform because it is specifically designed to handle uneven sampling. Standard Fourier transforms require uniformly sampled data, whereas Lomb-Scargle accurately detects periodic signals in irregularly spaced astronomical observations.