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arXiv 1602.00902astro-ph.SR
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用多层神经网络进行类日恒星基本参数的星震测定

Asteroseismic determination of fundamental parameters of sun-like stars using multi-layered neural networks

Kuldeep Verma, Shravan Hanasoge, Jishnu Bhattacharya, H M Antia, Ganapathy Krishnamurthi

摘要:

像CoRoT和开普勒这样的太空天文台的出现使我们能够在数千颗恒星上测试我们对恒星演化的理解。演化模型通常需要五个输入参数,质量,初始氦丰度,初始金属丰度,混合长度(假设随时间不变),以及恒星必须演化的年龄。其中一些参数在描述相关行星和研究银河系考古学方面也非常有用。如何从观测中快速而准确地获得这些参数,特别是在对数千颗恒星进行巡天的背景下,是一个突出的问题,一个没有直接解决的问题。对于一个给定的恒星,我们通常通过光谱测量有效温度和表面金属丰度,并通过空间天文台测量低阶振荡频率。在这里,我们证明了统计学习,使用人工神经网络,是成功的光谱和地震测量的基础上确定的进化参数。我们训练的网络在广泛的参数空间范围内表现出鲁棒性,而且关键的是,它完全是计算成本低廉和完全自动化的。我们用这种方法分析了几颗恒星的观测结果,其结果与用其他方法得到的推论一致。这种方法既计算成本低,又非常准确,为分析过去、当前和未来任务中的大量恒星观测结果铺平了道路。

英文摘要:

The advent of space-based observatories such as CoRoT and Kepler has enabled the testing of our understanding of stellar evolution on thousands of stars. Evolutionary models typically require five input parameters, the mass, initial Helium abundance, initial metallicity, mixing- length (assumed to be constant over time), and the age to which the star must be evolved. Some of these parameters are also very useful in characterizing the associated planets and in studying galactic archaeology. How to obtain these parameters from observations rapidly and accurately, specifically in the context of surveys of thousands of stars, is an outstanding ques- tion, one that has eluded straightforward resolution. For a given star, we typically measure the effective temperature and surface metallicity spectroscopically and low-degree oscillation frequencies through space observatories. Here we demonstrate that statistical learning, using artificial neural networks, is successful in determining the evolutionary parameters based on spectroscopic and seismic measurements. Our trained networks show robustness over a broad range of parameter space, and critically, are entirely computationally inexpensive and fully automated. We analyze the observations of a few stars using this method and the results com- pare well to inferences obtained using other techniques. This method is both computationally cheap and inferentially accurate, paving the way for analyzing the vast quantities of stellar observations from past, current, and future missions.

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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