The Planet Whisperer: Toward Characterizing Low-mass Planets from Doppler Surveys in the Presence of Stellar Activity

探测其他恒星周围的类地行星的一种方法是寻找当系外行星经过其星星前面时，星星的光信号或光谱发生的非常小的变化。然而，这些光谱受到许多噪声源的影响，这些噪声源阻止这些信号变化被检测到。天文学家的目标是开发出将系外行星的信号与噪声源分离的方法。最难识别和消除的恒星噪声是由星星表面随时间变化的斑点引起的。这些研究人员将寻找经过行星的信号与星星表面斑点信号之间的差异，并利用这些差异创建可以检测差异的计算机程序。调查人员将创建可用于测试其程序的虚假数据。如果成功，他们将应用这些软件工具搜索来自太空和地面望远镜的数据，他们希望在那里找到数百颗类地行星。这项研究通过开发和应用新的工具来探测可能存在生命的行星，从而为国家利益服务。作为这项研究的一部分而开发的技术将被纳入大学的课程和研究中。天文学家现在正在寻找类似地球的天体。在开发更稳定和更高分辨率的光谱仪方面取得了重大进展，这些光谱仪有可能提供足以探测地球类似物的精度。然而，恒星光谱中包含着来自恒星光球层的准周期信号：颗粒化、振荡、旋转恒星上的斑点和谱斑，以及日冕流。对于系外行星探测来说，这些恒星噪声信号中最具挑战性的是斑点和斑。研究人员建议系统地寻找行星的多普勒频移与光斑和斑的光球信号之间的差异。他们将使用盲源分离技术与主成分分析，独立成分分析，字典学习算法和多元高斯过程建模。将建立一个大型模拟数据库，并将其用于验证区分多普勒频移和光球噪声源的统计工具的功效。经过验证后，最有希望的算法将应用于来自天基和多个地基望远镜的真实的数据。如果能够在存在背景噪音源的情况下提取星载开普勒望远镜检测到的信号水平，将检测到数百颗类地行星。作为这项研究的一部分开发的统计技术将被纳入大学一级的课程和研究。

One way of detecting Earth-like planets around other stars is to look for very small shifts in the light signal, or spectrum, of a star that change when an exoplanet passes in front of its star. These spectra, however, are affected by many sources of noise that block these signal changes from being detected. Astronomers aim to develop ways of separating the signals of the exoplanets from the sources of noise. The hardest stellar noise to identify and remove is caused by spots that change with time on the star's surface. These investigators will look for differences between the signals of passing planets and signals of the surface spots on the star, and use these differences to create computer programs that can detect the differences. The investigators will create false data that can be used to test their programs. If successful, they will then apply these software tools to search data from space-based and ground-based telescopes, where they expect to find hundreds of Earth-like planets. This research serves the national interest by developing and applying new tools to detect planets that could harbor life. The techniques that are developed as part of this research will be included in classes and research at the university level.Astronomers are now searching for analogs of the Earth. Significant progress has been made in the development of more stable and higher resolution spectrometers with the potential to deliver precision sufficient to detect Earth analogs. However, stellar spectra are laced with quasi-periodic signals that arise from stellar photospheres: granulation, oscillations, spots and plage on rotating stars, and meridional flows. The most challenging of these stellar noise signals for exoplanet detection are spots and plage. The investigators propose to search systematically for differences between Doppler shifts from planets and the photospheric signals from spots and plage. They will use blind source separation techniques with principal component analysis, independent component analysis, dictionary learning algorithms, and multivariate Gaussian process modeling. A large library of simulated data will be created and used to verify the efficacy of the statistical tools for distinguishing Doppler shifts from photospheric noise sources. After validation, the most promising algorithms will be applied to real data from space-based and multiple ground-based telescopes. If signals of the level detected by the spaceborne Kepler telescope can be extracted in the presence of background noise sources, hundreds of Earth-like planets will be detected. The statistical techniques that are developed as part of this research will be incorporated into classes and research at the university level.