How does contaminante work?

contaminante uses pixel level modeling to model the systematics and astrophysics in each pixel. Each pixel is modeled by components that include:

• A B-spline in time (with knots every 2 days by default)
• A prediction of the centroid position, either using the Kepler Pipieline POSCORR values, or building an arclength model similar to that used in the self flat fielding method (see lightkurve.SFFCorrector)
• Optionally, an estimate of the scattered background light (useful for TESS data)
• Optionally, the top Cotrending Basis Vectors from the Kepler pipeline
• A transit model, with period, transit mid point and duration specified by the user.

These components create a design matrix, consisting of predictors of the systematics of the light curve.

In each pixel, contaminante finds the best fitting model \(m\) for each pixel, where \(m\) is given by

\[ m = S . X . w\]

where \(S\) is an estimate of the astrophysical flux, and \(X\) is the design matrix described above. \(w\) are the weights of each component. Using L2 regularization, contaminante finds the optimum values of \(w\) to find the best fitting model \(m\) in each pixel. Contaminante then samples to find the uncertainty of each weight \(\sigma_w\), assuming Gaussian errors. The weight for the transit model component in each pixel can then be interpretted as the strength of the transiting signal in each pixel. Using the uncertainty, contaminante identifies pixels where the transiting signal is measured at a significance >\(3\sigma\). These pixels are summed across every quarter, campaign or sector available to find simple aperture photometry of all pixels containing a significant transiting signal. contaminante then finds the source center and the original target center, and returns the measured transit depth in each light curve.