发表机构
NASA Ames Research Center; Research Institute for Advanced Computer Science, Universities Space Research Association; SETI Institute（美国国家航空航天局艾姆斯研究中心; 先进计算机科学研究院，大学空间研究协会; 搜寻地外文明研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出SysCoCoPy软件包，比较PDC与三种社区校正器对TESS光曲线系统误差的校正效果，发现PDC在散射光去除上更稳健。
AI 中文摘要
校正TESS光曲线中的系统误差对于所有使用这些观测数据的天体物理分析至关重要。在此，我们介绍一项数据分析及一个软件包SysCoCoPy，用以研究并直接比较来自科学处理操作中心（SPOC）流程的预搜索数据校正（PDC）方法与社区开发的三种校正器的性能。我们基于这三种校正器在Lightkurve中的实现将其整合，并特别关注四种校正器去除来自地球和月球的散射光污染的能力，这是TESS的一个关键系统误差。我们在SysCoCoPy中实现了这些校正器，其框架允许自动优化它们的参数，以将分析扩展到日益增大的样本。SysCoCoPy为单个案例的比较提供定性和定量产品，并为选定样本提供统计结果。目前我们发现，尽管我们的自动参数优化为其中两种设计上有利于此目的的校正器提供了大量成功的散射光校正，但对我们最大样本的统计分析表明，PDC目前更为稳健，其使用的指标总体成功水平更高。
英文摘要
The correction of systematic errors in TESS light curves is crucial for all astrophysical analyses employing these observations. Here we present a data analysis and a software package, SysCoCoPy, to investigate and directly compare the performance of the Presearch Data Conditioning (PDC) correcting method from the Science Processing Operations Center (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 the ability of the four correctors to remove scattered light contamination from the Earth and the Moon, which is a key systematic for TESS. We implemented these correctors in SysCoCoPy with a framework that allows an automatic optimization of their parameters to scale the analysis towards increasingly larger samples. SysCoCoPy provides qualitative and quantitative products for the comparison of individual cases as well as statistical results for selected samples. We currently find that while 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, an statistical analysis of our largest sample indicates that PDC is presently more robust, with a larger overall success level of the metrics used.
Comments16 pages, 6 figures. Code available at https://github.com/drapetti/SysCoCoPy
