旋转恒星核坍缩波形的主成分分析
Rotating stellar core-collapse waveform decompositon: a Principal Component Analysis approach
摘要：
本文介绍了使用主成分分析作为一种方法来分解的波形目录，以产生一组正交基向量。我们将这种方法应用于一组超新星波形，并将得到的基向量与通过Gram-Schmidt分解得到的基向量进行比较。我们观察到，对于所选的一组波形，这两种方法的性能相当的最小匹配要求高达0.9，14克-施密特基向量和12个主成分所需的最小匹配0.9。这意味着在所选波形中存在许多共同特征。此外，我们观察到所选择的波形具有非常相似的特征，并且可以通过仅分解所选择的目录中的整个波形集合的三分之一来获得0.7的最小匹配。我们讨论了这一观察的影响和主成分分析的特征分解波形目录的优点。
英文摘要：
This paper introduces the use of Principal Component Analysis as a method to decompose the waveform catalogues to produce a set of orthonormal basis vectors. We apply this method to a set of supernova waveforms and compare the basis vectors obtained with those obtained through Gram-Schmidt decomposition. We observe that, for the chosen set of waveforms, the performance of the two methods are comparable for minimal match requirements up to 0.9, with 14 Gram-Schmidt basis vectors and 12 principal components required for a minimal match of 0.9. This implies that there are many common features in the chosen waveforms. Additionally, we observe the chosen waveforms have very similar features and a minimal match of 0.7 can be obtained by decomposing only one third of the entire set of waveforms in the chosen catalogue. We discuss the implications of this observation and the advantages of eigen-decomposing waveform catalogues with Principal Component Analysis.
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。
