transit-least-squares

You can detect exoplanet transits in light curve data by fitting physical transit models across a period grid. This approach searches for transit-shaped dips in photometric time series, returning the best-fit period, depth, and signal metrics for validation.

To run a transit period search, you need to provide time-series photometry containing time, flux, and flux_err arrays. These inputs allow the algorithm to fit transit models and retrieve period, T0, depth, SNR, and SDE metrics.

Transit least squares is designed for time-series photometry with gaps and varying noise. It fits transit models across these imperfections, making it suitable for real-world light curves from missions like Kepler and TESS.

Transit least squares outperforms generic periodograms for exoplanet discovery because it fits transit-shaped dips specifically. Unlike generic methods, it applies physical transit models across period and duration grids to validate candidate signals accurately.

You can search for multiple planets using a mask-and-reanalyze workflow. After identifying the primary transit signal, you mask it and reanalyze the light curve to detect additional periodic transit events.

Transit least squares outputs period, T0, depth, SNR, and SDE metrics for candidate validation. These results characterize the fitted transit model and help confirm the presence of exoplanet signals in light curves.