  The majority of observed pixels on the Transiting Exoplanet Survey Satellite
(TESS) are delivered in the form of full frame images (FFI). However, the FFIs
contain systematic effects such as pointing jitter and scattered light from the
Earth and Moon that must be removed before downstream analysis. We present
unpopular, an open-source Python package to de-trend TESS FFI light curves
based on the causal pixel model method. Under the assumption that shared flux
variations across multiple distant pixels are likely to be systematics,
unpopular removes these common (i.e., popular) trends by modeling the
systematics in a given pixel's light curve as a linear combination of light
curves from many other distant pixels. To prevent overfitting we employ ridge
regression and a train-and-test framework where the data points being
de-trended are separated from those used to obtain the model coefficients. We
also allow for simultaneous fitting with a polynomial model to capture any
long-term astrophysical trends. We validate our method by de-trending different
sources (e.g., supernova, tidal disruption event, exoplanet-hosting star, fast
rotating star) and comparing our light curves to those obtained by other
pipelines when appropriate. We also show that unpopular is able to preserve
sector-length astrophysical signals, allowing for the extraction of
multi-sector light curves from the FFI data. The unpopular source code and
tutorials are freely available online.
