box-least-squares

Apply a Box Least Squares periodogram search to your photometric time series to detect periodic box-shaped transit signals. It automatically estimates period, duration, depth, and reference time for exoplanet and eclipsing-binary candidates.

The Box Least Squares method fits periodic box-shaped signals to photometric time series data to estimate transit period, duration, depth, and reference time. It provides validation statistics to help assess the detected exoplanet transit candidates.

Yes, you can use astropy BoxLeastSquares for eclipsing binary searches because it detects periodic box-shaped signals in photometric time series. The method applies to both exoplanet transit detection and eclipsing-binary light curve analysis.

Validate transit candidates by applying compute_stats() to the detected Box Least Squares peaks. This provides validation statistics for transit timing, depth, and duration to confirm periodic box-shaped signals in photometric time series data.

BLS detects periodic box-shaped transit signals in light curves, while Lomb-Scargle fits sinusoidal variations in time series. The BLS periodogram is designed for exoplanet transit detection and can be cross-checked with Lomb-Scargle during surveys.

Yes, you need numpy and astropy to run a BLS periodogram search. The Skill requires astropy BoxLeastSquares for core transit fitting and numpy for array handling, with optional plotting libraries for visualizing light curve results.