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Other literature type . 2024
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Presentation . 2024
License: CC BY
Data sources: Datacite
ZENODO
Presentation . 2024
License: CC BY
Data sources: Datacite
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Comparing and Automatically Optimizing the Performance of Systematic Error Correctors for TESS Light Curves

Authors: Rapetti, David; Jenkins, Jon; Twicken, Joseph; Caldwell, Douglas;

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

Abstract

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.

Keywords

TESS mission, Data Analysis Techniques, Systematic Error Corrections, Exoplanets, Corrected Light Curves

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green