Application of data science techniques to disentangle X-ray spectral variation of super-massive black holes

We apply three data science techniques, Nonnegative Matrix Factorization (NMF), Principal Component Analysis (PCA) and Independent Component Analysis (ICA), to simulated X-ray energy spectra of a particular class of super-massive black holes. Two competing physical models, one whose variable compone...

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Bibliographic Details
Published in:arXiv.org 2017-01
Main Authors: Pike, S, Ebisawa, K, Ikeda, S, Morii, M, Mizumoto, M, Kusunoki, E
Format: Article
Language:English
Subjects:
Computer simulation
Data science
Energy spectra
Independent component analysis
Mathematical models
Matrix methods
Model accuracy
Principal components analysis
Supermassive black holes
Viability
X ray spectra
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Summary:We apply three data science techniques, Nonnegative Matrix Factorization (NMF), Principal Component Analysis (PCA) and Independent Component Analysis (ICA), to simulated X-ray energy spectra of a particular class of super-massive black holes. Two competing physical models, one whose variable components are additive and the other whose variable components are multiplicative, are known to successfully describe X-ray spectral variation of these super-massive black holes, within accuracy of the contemporary observation. We hope to utilize these techniques to compare the viability of the models by probing the mathematical structure of the observed spectra, while comparing advantages and disadvantages of each technique. We find that PCA is best to determine the dimensionality of a dataset, while NMF is better suited for interpreting spectral components and comparing them in terms of the physical models in question. ICA is able to reconstruct the parameters responsible for spectral variation. In addition, we find that the results of these techniques are sufficiently different that applying them to observed data may be a useful test in comparing the accuracy of the two spectral models.
ISSN:2331-8422