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The majority of observed pixels on the Transiting Exoplanet Survey Satellite(TESS) are delivered in the form of full frame images (FFI). However, the FFIscontain systematic effects such as pointing jitter and scattered light from theEarth and Moon that must be removed before downstream analysis. We presentunpopular, an open-source Python package to de-trend TESS FFI light curvesbased on the causal pixel model method. Under the assumption that shared fluxvariations across multiple distant pixels are likely to be systematics,unpopular removes these common (i.e., popular) trends by modeling thesystematics in a given pixel's light curve as a linear combination of lightcurves from many other distant pixels. To prevent overfitting we employ ridgeregression and a train-and-test framework where the data points beingde-trended are separated from those used to obtain the model coefficients. Wealso allow for simultaneous fitting with a polynomial model to capture anylong-term astrophysical trends. We validate our method by de-trending differentsources (e.g., supernova, tidal disruption event, exoplanet-hosting star, fastrotating star) and comparing our light curves to those obtained by otherpipelines when appropriate. We also show that unpopular is able to preservesector-length astrophysical signals, allowing for the extraction ofmulti-sector light curves from the FFI data. The unpopular source code andtutorials are freely available online.

The unpopular Package: a Data-driven Approach to De-trend TESS Full Frame Image Light Curves