pyaneti - II. A multidimensional Gaussian process approach to analysing spectroscopic time-series
pyaneti - II. A multidimensional Gaussian process approach to analysing spectroscopic time-series
复制标题
皮亚内提-II。
DOI：
10.1093/mnras/stab2889
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发表时间：
2022
影响因子：
4.8
通讯作者：
Barragán O
中科院分区：
文献类型：
--
作者：
Barragán O
两种最成功的系外行星探测方法依赖于在光度和径向速度时间序列中探测行星信号。这取决于利用数据和理论之间的协同作用来估计行星，轨道和/或恒星参数的数值技术。在这项工作中，我们提出了一个新版本的系外行星建模codepyaneti。这一新的新闻稿特别强调径向速度时间序列中恒星信号的建模。该代码有一个内置的多维高斯过程的方法来模拟径向速度和活动指标的时间序列与不同的基础协方差函数。这个新版本的代码还允许多波段和单次传输建模;它运行在Python 3上，并具有整体性能的改进。我们描述了新的实现，并提供测试，以验证新的例程，有直接应用到系外行星的检测和表征。我们已将代码公开，并在https://github.com/oscaribv/pyaneti上免费提供。我们还提出了codescitlavelueandcitlalatonac，允许一个创建合成光度和光谱时间序列，分别与行星和恒星的信号。
The two most successful methods for exoplanet detection rely on the detection of planetary signals in photometric and radial velocity time-series. This depends on numerical techniques that exploit the synergy between data and theory to estimate planetary, orbital, and/or stellar parameters. In this work, we present a new version of the exoplanet modelling codepyaneti. This new release has a special emphasis on the modelling of stellar signals in radial velocity time-series. The code has a built-in multidimensional Gaussian process approach to modelling radial velocity and activity indicator time-series with different underlying covariance functions. This new version of the code also allows multiband and single transit modelling; it runs onPython 3, and features overall improvements in performance. We describe the new implementation and provide tests to validate the new routines that have direct application to exoplanet detection and characterization. We have made the code public and freely available at https://github.com/oscaribv/pyaneti. We also present the codescitlalicueandcitlalatonacthat allow one to create synthetic photometric and spectroscopic time-series, respectively, with planetary and stellar-like signals.