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arXiv 1711.03329astro-ph.IMastro-ph.EP
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系外行星科学的贝叶斯方法

Bayesian Methods for Exoplanet Science

Hannu Parviainen

摘要:

系外行星研究是在现有望远镜和仪器能力的极限下进行的。所研究的信号是微弱的,往往嵌入在复杂的系统,从仪器,大地和天体物理来源。结合对周期事件的重复观测、多台望远镜的同时观测、不同的观测技术以及来自理论和先前研究的现有信息,可以帮助从行星信号中理清系统学,并提供优于单独分析观测的协同优势。贝叶斯推断提供了一个自洽的统计框架,解决了复杂的系统模型的必要性,并需要结合先验信息和异构的观察。本章简要介绍了系外行星研究背景下的贝叶斯推断,重点是时间序列分析,并概述了一组免费提供的编程库。

英文摘要:

Exoplanet research is carried out at the limits of the capabilities of current telescopes and instruments. The studied signals are weak, and often embedded in complex systematics from instrumental, telluric, and astrophysical sources. Combining repeated observations of periodic events, simultaneous observations with multiple telescopes, different observation techniques, and existing information from theory and prior research can help to disentangle the systematics from the planetary signals, and offers synergistic advantages over analysing observations separately. Bayesian inference provides a self-consistent statistical framework that addresses both the necessity for complex systematics models, and the need to combine prior information and heterogeneous observations. This chapter offers a brief introduction to Bayesian inference in the context of exoplanet research, with focus on time series analysis, and finishes with an overview of a set of freely available programming libraries.

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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