TESS Data for Asteroseismology (T’DA) Stellar Variability Classification Pipeline: Setup and Application to the Kepler Q9 Data
TESS Data for Asteroseismology (T’DA) Stellar Variability Classification Pipeline: Setup and Application to the Kepler Q9 Data
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DOI：
10.3847/1538-3881/ac166a
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
J. Audenaert;J. Kuszlewicz;R. Handberg;A. Tkachenko;David J Armstrong;M. Hon;R. Kgoadi;M. Lund;K. Bell;L. Bugnet;D. Bowman;C. Johnston;R. A. Garc'ia;D. Stello;L. Moln'ar;E. Plachy;D. Buzasi;C. Aerts;the T'DA collaboration
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作者：
J. Audenaert;J. Kuszlewicz;R. Handberg;A. Tkachenko;David J Armstrong;M. Hon;R. Kgoadi;M. Lund;K. Bell;L. Bugnet;D. Bowman;C. Johnston;R. A. Garc'ia;D. Stello;L. Moln'ar;E. Plachy;D. Buzasi;C. Aerts;the T'DA collaboration
美国宇航局凌日系外行星调查卫星(TESS)正在观测数以千万计的恒星，持续观测的时间跨度从∼27天到大约1年不等。这些海量的数据包含了丰富的可变性、系外行星和恒星天体物理研究的信息，但在充分利用这些信息之前，需要进行一些处理步骤。为了有效地处理所有TESS数据并提供给更广泛的科学界，TESS天体地震学数据工作组作为TESS天体地震科学联合会的一部分，创建了一个自动化的开源处理管道，以便从短和长节奏原始光度测量数据中产生经过系统学校正的光曲线，并根据恒星变率类型对这些曲线进行分类。我们将对TESS星等下至15等的所有恒星进行处理。本文是详细介绍流水线如何工作的系列文章的下一篇。在这里，我们提出了我们的方法，用于TESS光度学的自动变异性分类，使用的是被组合成一个元分类器的有监督的学习者集合。我们成功地使用了一个精心构建的开普勒Q9光曲线的标记样本来验证我们的方法，该样本的时间跨度为27.4天，模拟了单扇区TESS观测，获得了94.9%的总体准确率。我们证明，我们的方法可以成功地分类我们标记的样本之外的恒星，方法是将其应用于开普勒太空任务Q9中观察到的所有∼167,000颗恒星。
The NASA Transiting Exoplanet Survey Satellite (TESS) is observing tens of millions of stars with time spans ranging from ∼27 days to about 1 yr of continuous observations. This vast amount of data contains a wealth of information for variability, exoplanet, and stellar astrophysics studies but requires a number of processing steps before it can be fully utilized. In order to efficiently process all the TESS data and make it available to the wider scientific community, the TESS Data for Asteroseismology working group, as part of the TESS Asteroseismic Science Consortium, has created an automated open-source processing pipeline to produce light curves corrected for systematics from the short- and long-cadence raw photometry data and to classify these according to stellar variability type. We will process all stars down to a TESS magnitude of 15. This paper is the next in a series detailing how the pipeline works. Here, we present our methodology for the automatic variability classification of TESS photometry using an ensemble of supervised learners that are combined into a metaclassifier. We successfully validate our method using a carefully constructed labeled sample of Kepler Q9 light curves with a 27.4 days time span mimicking single-sector TESS observations, on which we obtain an overall accuracy of 94.9%. We demonstrate that our methodology can successfully classify stars outside of our labeled sample by applying it to all ∼167,000 stars observed in Q9 of the Kepler space mission.