lightkurve.correctors.RegressionCorrector.correct

RegressionCorrector.correct(design_matrix_collection, cadence_mask=None, sigma=5, niters=5, propagate_errors=False)

Find the best fit correction for the light curve.

Parameters
design_matrix_collectionDesignMatrix or DesignMatrixCollection

One or more design matrices. Each matrix must have a shape of (time, regressors). The columns contained in each matrix must be known to correlate with additive noise components we want to remove from the light curve.

cadence_masknp.ndarray of bools (optional)

Mask, where True indicates a cadence that should be used.

sigmaint (default 5)

Standard deviation at which to remove outliers from fitting

nitersint (default 5)

Number of iterations to fit and remove outliers

propagate_errorsbool (default False)

Whether to propagate the uncertainties from the regression. Default is False. Setting to True will increase run time, but will sample from multivariate normal distribution of weights.

Returns
corrected_lcLightCurve

Corrected light curve, with noise removed.