http://swrc.ontoware.org/ontology#TechnicalReport
Application of data science techniques to disentangle of X-ray spectral variation of super-massive black holes
en
Pike Sean
海老沢 研
池田 思朗
森井 幹雄
水本 岬希
楠 絵莉子
Pike Sean
Ebisawa Ken
Ikeda Shiro
Morii Mikio
Mizumoto Misaki
Kusunoki Eriko
宇宙航空研究開発機構(JAXA)
Japan Aerospace Exploration Agency (JAXA)
JAXA Research and Development Report: Journal of Space Science Informatics Japan: Volume 6
JAXA-RR-16-007
73-87
2017-03-17
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.
形態: カラー図版あり
Physical characteristics: Original contains color illustrations
1349-1113
AA1192675X
資料番号: AA1630049007
レポート番号: JAXA-RR-16-007