DIAMONDS: A new Bayesian nested sampling tool Application to peak bagging of solar-like oscillations
DIAMONDS: A new Bayesian nested sampling tool Application to peak bagging of solar-like oscillations
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DOI：
10.1051/0004-6361/201424181
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发表时间：
2014-11-01
影响因子：
6.5
通讯作者：
De Ridder, J.
中科院分区：
文献类型：
--
作者：
Corsaro, E.;De Ridder, J.
上下文。由于CoRoT和NASA开普勒太空任务的出现，类似太阳振荡的星震学现在是我们理解恒星物理的基础。尤其是开普勒航天器，在高占空比下发布了长达三年多的优秀光度观测数据，其中包含了大量尚未调查的信息。为了充分发挥开普勒光线曲线的潜力，现在比以往任何时候都更需要复杂和强大的分析工具。以前所未有的精度表征单星，并随后详细分析恒星群，是进一步约束恒星结构和演化模型的基础。我们利用嵌套抽样蒙特卡罗(NSMC)算法开发了一种新的贝叶斯参数估计和模型比较程序，称为DIANCES，这是一种非常适合于高维和多峰问题的有效方法。对代码中实现的功能进行了详细描述，重点介绍了与其他基于NSMC的现有方法的新颖性和差异。然后在明亮的F8V星KIC 9139163上测试钻石，这是一个具有挑战性的峰值袋装分析目标，因为它观察到大量的振荡峰，这些振荡峰与L=2；0峰和强烈的恒星背景信号之间的混合发生在一起。我们采用了1147.5天的开普勒光曲线，在恒星的功率谱中占据了超过840000个数据箱，进一步测试了该方法的性能。钻石编码能够为KIC 9139163的峰值装袋分析提供稳健的结果，同时保持相当大的计算效率来识别解。我们测试了恒星中不同天体物理背景的探测，并提供了一个基于贝叶斯证据的准则，用于详细评估探测到的振荡的峰值重要性。我们给出了59个独立振荡频率、幅度和线宽的结果，并与文献中现有的值进行了详细的比较，当使用不同的背景时，发现这些值有显著的偏差。最后，我们成功地展示了一种创新的峰值装袋方法，该方法利用了钻石对多峰分布进行采样的能力，这对于未来可能实现分析技术的自动化具有很大的潜力。
Context. Thanks to the advent of the space-based missions CoRoT and NASA's Kepler, the asteroseismology of solar-like oscillations is now at the base of our understanding about stellar physics. The Kepler spacecraft, especially, is releasing excellent photometric observations of more than three years length in high duty cycle, which contain a large amount of information that has not yet been investigated.Aims. To exploit the full potential of Kepler light curves, sophisticated and robust analysis tools are now required more than ever. Characterizing single stars with an unprecedented level of accuracy and subsequently analyzing stellar populations in detail are fundamental to further constrain stellar structure and evolutionary models.Methods. We developed a new code, termed DIAMONDS, for Bayesian parameter estimation and model comparison by means of the nested sampling Monte Carlo (NSMC) algorithm, an efficient and powerful method very suitable for high-dimensional and multi-modal problems. A detailed description of the features implemented in the code is given with a focus on the novelties and differences with respect to other existing methods based on NSMC. DIAMONDS is then tested on the bright F8 V star KIC 9139163, a challenging target for peak-bagging analysis due to its large number of oscillation peaks observed, which are coupled to the blending that occurs between l = 2; 0 peaks, and the strong stellar background signal. We further strain the performance of the approach by adopting a 1147.5 days-long Kepler light curve, accounting for more than 840 000 data bins in the power spectrum of the star.Results. The DIAMONDS code is able to provide robust results for the peak-bagging analysis of KIC 9139163, while preserving a considerable computational efficiency for identifying the solution at the same time. We test the detection of different astrophysical backgrounds in the star and provide a criterion based on the Bayesian evidence for assessing the peak significance of the detected oscillations in detail. We present results for 59 individual oscillation frequencies, amplitudes and linewidths and provide a detailed comparison to the existing values in the literature, from which significant deviations are found when a different background is used. Lastly, we successfully demonstrate an innovative approach to peak bagging that exploits the capability of DIAMONDS to sample multi-modal distributions, which is of great potential for possible future automatization of the analysis technique.