Comparing and Automatically Optimizing the Performance of Systematic Error Correctors for TESS Light Curves

The correction of systematic errors in TESS light curves is crucial for all astrophysical analyses employing these observations. Here I will present a data analysis and a software package to investigate and directly compare the performance of the PDCSAP correcting method from the SPOC pipeline and three correctors developed by the community. We incorporate these three correctors based on their implementations in Lightkurve and are particularly interested in their ability to remove scattered light contamination from the Earth and the Moon, which is a key systematic for TESS. We implemented these correctors in a framework that allows an automatic optimization of their parameters to scale the analysis towards increasingly larger samples. Our package provides qualitative and quantitative products for the comparison of individual cases as well as statistical results for selected samples. We find that our automatic parameter optimization provides a significant number of successful scattered-light corrections for two of the correctors with a design that favors this purpose that can even allow the recovery of certain cadences excluded by PDCSAP due to high contamination. I will present initial results for selected cases and samples.