box-least-squares

A BLS periodogram detects periodic box-shaped dips in photometric time series to reveal transiting exoplanets. It searches light curve data across configurable period and duration grids to fit a box-shaped transit model and identify candidate events.

You can search a light curve for transiting exoplanets by running a BLS periodogram on your photometric time series. The process uses Astropy's BoxLeastSquares to scan configurable period ranges and output best-fit parameters like period, duration, and transit time.

Yes, BLS transit detection is designed to analyze large photometric survey datasets from missions like TESS and Kepler. It efficiently searches time series data across a range of periods and durations to flag initial transit candidates for follow-up validation.

A box least squares search provides periodogram power, best-fit parameters including period and duration, and basic validation statistics. These outputs help assess the reliability of transit-like events detected in the photometric time series.

The best way to find eclipsing binaries in astronomical time series data is applying a BLS periodogram. It detects periodic box-shaped dips in photometric observations, effectively identifying both transiting exoplanets and eclipsing binary systems.

You should not use a box least squares periodogram when transit signals lack a box-shaped dip profile or when the photometric time series has irregular cadence. BLS specifically fits box-shaped models, making it less effective for non-box-like stellar variability signals.