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arXiv 2102.10326astro-ph.EPastro-ph.IMcs.LG
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用机器学习方法自动识别NASA凌日系外行星勘测卫星(TESS)数据中的凌日系外行星候选者

Automated identification of transiting exoplanet candidates in NASA Transiting Exoplanets Survey Satellite (TESS) data with machine learning methods

Catholic University of America(美国天主教大学);Tel Aviv University(特拉维夫大学);ThetaRay(ThetaRay公司)

Leon Ofman, Amir Averbuch, Adi Shliselberg, Idan Benaun, David Segev, Aron Rissman

摘要:

一种使用机器学习(ML)方法的新型人工智能(AI)技术结合了由ThetaRay,Inc.应用于NASA的凌日系外行星勘测卫星(TESS)数据集,以识别系外行星候选者。AI/ML ThetaRay系统最初使用开普勒系外行星数据进行训练,并在应用于TESS数据之前使用确认的系外行星进行验证。数据的现有和新特征基于各种观测参数,通过采用半监督和无监督机器学习技术构建并用于AI/ML分析。通过将ThetaRay系统应用于TESS任务产生的10,803个阈值交叉事件(TCEs)的光变曲线,该算法从Mikulski Archive for Space Telescopes获得,该算法产生了约50个目标以供进一步分析,我们通过进一步的人工审查发现了三个新的系外行星候选者。这项研究首次展示了将特定的基于AI/ML的方法成功应用于大型天体物理数据集,以快速自动分类TCE。

英文摘要:

A novel artificial intelligence (AI) technique that uses machine learning (ML) methodologies combines several algorithms, which were developed by ThetaRay, Inc., is applied to NASA's Transiting Exoplanets Survey Satellite (TESS) dataset to identify exoplanetary candidates. The AI/ML ThetaRay system is trained initially with Kepler exoplanetary data and validated with confirmed exoplanets before its application to TESS data. Existing and new features of the data, based on various observational parameters, are constructed and used in the AI/ML analysis by employing semi-supervised and unsupervised machine learning techniques. By the application of ThetaRay system to 10,803 light curves of threshold crossing events (TCEs) produced by the TESS mission, obtained from the Mikulski Archive for Space Telescopes, the algorithm yields about 50 targets for further analysis, and we uncover three new exoplanetary candidates by further manual vetting. This study demonstrates for the first time the successful application of the particular combined multiple AI/ML-based methodologies to a large astrophysical dataset for rapid automated classification of TCEs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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