transit-least-squares

Detect exoplanet transits in a light curve by fitting physical transit models across different periods, durations, and epochs using TLS to identify repeating dips in stellar brightness. TLS searches for periodic transit-like signals in photometric time series.

TLS transit detection improves sensitivity over generic periodogram methods by fitting actual transit models to the light curve rather than relying on generic signal detection. This physical transit model fitting optimizes the identification of exoplanet transit signals.

Yes, you can use TLS for single or multi-planet searches in photometric time series from missions like Kepler or TESS. It processes time, flux, and flux_err arrays to identify repeating transit-like dips in varied data quality scenarios.

To run a TLS period search, provide time, flux, and flux_err arrays and call the power() method. Optional parameters include period ranges, oversampling factors, and T0 margins to refine the transit signal detection.

Transit least squares signal detection requires the Python packages transitleastsquares and lightkurve. Numpy and matplotlib are also needed for utility functions when processing the stellar light curves.

TLS estimates the period, depth, and epoch of a transiting planet from the light curve. It identifies a repeating dip in stellar brightness that matches a planet transit model to extract these specific physical parameters.