box-least-squares

The BoxLeastSquares periodogram detects periodic transit signals in stellar light curves by identifying box-shaped dips in photometric time-series data, automatically searching across durations and periods to estimate optimal transit parameters.

For transit detection validation, you can compute statistics via compute_stats to evaluate transit depth, signal-to-noise ratio, and odd-even consistency. These metrics help confirm whether a detected periodic dip is a genuine transit candidate or a false positive.

BoxLeastSquares specifically detects box-shaped transit dips in light curves, whereas Lomb-Scargle fits sinusoidal periodic signals. You can perform a comparative analysis of BLS results against Lomb-Scargle or TLS to assess sensitivity and robustness for your specific stellar data.

Yes, you need Astropy to run these box least squares periodograms because the Skill implements its transit detection logic directly on top of Astropy's BoxLeastSquares module to process time and flux data.

Yes, you can use the autopower function to automatically search for transit periods by providing your time and flux data alongside a chosen transit duration, then inspecting the peak period in the resulting periodogram to identify transit candidates.

To avoid false positives in exoplanet transit searches, you should use compute_stats to check odd-even consistency and signal-to-noise ratio, perform phase-fold visualization to confirm the transit shape, and follow provided guidance on period-grid choices and false-positive checks.