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arXiv 1807.08600astro-ph.HE
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Blazar星X射线光谱变率的主成分分析

X-ray spectral variability of blazars using principal component analysis

Dennis Gallant, Luigi C. Gallo, Michael L. Parker

摘要:

本文应用主成分分析(PCA)方法研究了各种耀变体的X射线光谱变化。对来自9个不同物体的数据进行了两种分析:长期分析,考察几年或几十年的变化趋势;短期分析,考察一次观测中的变化。然后将结果与模拟光谱进行比较,以识别它们对应的物理成分。它被发现,所有对象的长期变化是由一个单一的幂律分量的变化占主导地位。第一分量对每个天体的变异性有84%以上的影响,而第二分量至少有3%的影响,这些分量形状上的微小差异可以用来预测光谱参数相对于彼此变化的程度以及光谱硬度和通量之间的相关性等质量。短期变异性不那么明确,一些主成分分析结果没有明显的物理类似物。我们讨论了模拟过程,并特别评论PCA的线性假设的崩溃的后果,以及它如何体现在实际数据中。我们的结论是,PCA是一个有用的工具,用于分析变异性,但只有当其基本假设和局限性的理解。

英文摘要:

Principal Component Analysis (PCA) is applied to a variety of blazars to examine X-ray spectral variability. Data from nine different objects are analysed in two ways: long-term, which examines variability trends across years or decades, and short-term, which looks at variability within a single observation. The results are then compared to simulated spectra in order to identify the physical components that they correspond to. It is found that long-term variability for all objects is dominated by changes in a single power law component. The primary component is responsible for more than 84 per cent of the variability in every object, while the second component is responsible for at least 3 per cent. Small differences in the shapes of these components can be used to predict qualities such as the degree to which spectral parameters are varying relative to one another, and correlations between spectral hardness and flux. Short-term variability is less clear-cut, with no obvious physical analogue for some of the PCA results. We discuss the simulation process, and specifically remark on the consequences of the breakdown of the linearity assumption of PCA and how it manifests in the real data. We conclude that PCA is a useful tool for analysing variability, but only if its underlying assumptions and limitations are understood.

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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