Rotating stellar core-collapse waveform decomposition: a principal component analysis approach
Rotating stellar core-collapse waveform decomposition: a principal component analysis approach
复制标题
旋转恒星核心塌陷波形分解：主成分分析方法
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
I. Heng
中科院分区：
文献类型：
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作者：
I. Heng
本文介绍了使用主成分分析（PCA）作为分解重力波形目录以产生一组正交基向量的方法。我们将 PCA 应用于旋转恒星核心塌陷模拟产生的一组引力波形，并将其基本向量与通过 Gram-Schmidt 分解获得的基本向量进行比较。使用匹配参数进行比较，该参数量化一组基本向量重建每个波形的程度。如果我们要求以 0.9 或更好的匹配度重建目录中的所有波形，则这两种方法的性能与所需的 14 个 Gram-Schmidt 基向量和 12 个主成分相当。此外，我们观察到所选波形集具有非常相似的特征，并且通过仅分解 A = 2 的模拟生成的波形可以获得至少 0.7 的匹配。我们讨论了这一观察结果的含义以及使用 PCA 特征分解波形目录的优点。
This paper introduces the use of principal component analysis (PCA) as a method to decompose the catalogues of gravitational waveforms to produce a set of orthonormal basis vectors. We apply PCA to a set of gravitational waveforms produced by rotating stellar core-collapse simulations and compare its basis vectors with those obtained through Gram–Schmidt decomposition. The comparison is made using the match parameter which quantifies how well each waveform is reconstructed by a set of basis vectors. The performance of the two methods is found to be comparable with 14 Gram–Schmidt basis vectors and 12 principal components required if we require all waveforms in the catalogue to be reconstructed with a match of 0.9 or better. Additionally, we observe that the chosen set of waveforms has very similar features, and a match of at least 0.7 can be obtained by decomposing only waveforms generated from simulations with A = 2. We discuss the implications of this observation and the advantages of eigen-decomposing waveform catalogues with PCA.
影响因子：
8.6
作者：
H. Dimmelmeier;C. Ott;H.-Th. Janka;A. Marek;E. Müller
通讯作者：
E. Müller