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arXiv 2211.02428astro-ph.SRastro-ph.EPastro-ph.IM
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基于Kepler星的旋转调制和恒星噪声分形分类器

Fractal signature as a rotational modulation and stellar noise classifier based on the active Kepler stars

Paulo Cleber Farias da Silva Filho, Jose Ribamar Dantas Silveira Junior, Bricio Warney de Freitas Alves, Fernando Jose Silva Lima Filho, Vitor Marcelo Belo Ferr… 展开作者

Paulo Cleber Farias da Silva Filho, Jose Ribamar Dantas Silveira Junior, Bricio Warney de Freitas Alves, Fernando Jose Silva Lima Filho, Vitor Marcelo Belo Ferreira, Luiz Daniel Alves Rios, Thiago de Melo Santiago, Daniel Brito de Freitas

摘要:

在这项研究中,我们报告了701颗恒星在太阳附近的分析定义在三个类别,即太阳下,太阳和太阳超日旋转周期在1和70天之间,根据旋转调制信号推断的时间序列从开普勒任务的公共档案。在我们的分析中,我们进行了初步选择的基础上的旋转周期和位置的周期-H图,其中H表示赫斯特指数提取的分形分析。为了改进我们的分析,我们应用了一种称为R/S方法的分形方法,考虑到与不同时间间隔的光度调制相关的特征的波动以及我们样本时间序列中存在的分形痕迹。从这个意义上说,我们计算了所谓的赫斯特指数的参考星,发现它可以提供一个强大的判别旋转调制和背景噪声的行为,超越了什么可以实现单独的旋转周期本身。此外,我们的结果强调,恒星的旋转周期是由指数H的增加,在旋转周期的增加而增加。最后,我们的方法表明,所提到的指数可能是一个强大的旋转调制和噪声分类。

英文摘要:

In this study, we report on the analysis of 701 stars in a solar vicinity defined in three categories namely subsolar, solar, and supersolar with rotation periods between 1 and 70 days, based on rotational modulation signatures inferred from time series from the Kepler mission's Public Archives. In our analysis, we performed an initial selection based on the rotation period and position in the period-H diagram, where H denotes the Hurst exponent extracted from fractal analysis. To refine our analysis, we applied a fractal approach known as the R/S method, taking into account the fluctuations of the features associated with photometric modulation at different time intervals and the fractality traces that are present in the time series of our sample. In this sense, we computed the so-called Hurst exponent for the referred stars and found that it can provide a strong discriminant of rotational modulation and background noise behavior, going beyond what can be achieved with solely the rotation period itself. Furthermore, our results emphasize that the rotation period of stars is scaled by the exponent H which increases following the increase in the rotation period. Finally, our approach suggests that the referred exponent may be a powerful rotational modulation and noise classifier.

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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