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

Material type: 文書・図像類

Author: Pike, Seanほか

Publisher: 宇宙航空研究開発機構(JAXA)

Publication date: 2017-03-17

Material Format: Digital

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Note (General)：: We apply three data science techniques, Nonnegative Matrix Factorization (NMF), Principal Component Analysis (PCA) and Independent Component Analysis ...

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Material Type: 文書・図像類
Title: Application of data science techniques to disentangle of X-ray spectral variation of super-massive black holes
Author/Editor: Pike, Sean
海老沢, 研
池田, 思朗
森井, 幹雄
水本, 岬希
楠, 絵莉子
Ebisawa, Ken
Ikeda, Shiro
Morii, Mikio
Mizumoto, Misaki
Kusunoki, Eriko
Author Heading: Pike, Sean
海老沢, 研
池田, 思朗
森井, 幹雄
水本, 岬希
楠, 絵莉子
Ebisawa, Ken
Ikeda, Shiro
Morii, Mikio
Mizumoto, Misaki
Kusunoki, Eriko
Publication, Distribution, etc.: 宇宙航空研究開発機構(JAXA)
Publication Date: 2017-03-17
Publication Date (W3CDTF): 2017-03-17
Periodical title: 宇宙航空研究開発機構研究開発報告: 宇宙科学情報解析論文誌: 第6号 = JAXA Research and Development Report: Journal of Space Science Informatics Japan: Volume 6
No. or year of volume/issue: JAXA-RR-16-007
Volume: JAXA-RR-16-007
Pages: 73-87
ISSN (Periodical Title): ISSN : 1349-1113
Text Language Code: eng
Target Audience: 一般
Note (General): 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
資料番号: AA1630049007
レポート番号: JAXA-RR-16-007
DOI: 10.20637/JAXA-RR-16-007/0007
Access Restrictions: 限定公開
Periodical Title (NCID): AA1192675X
https://ci.nii.ac.jp/ncid/AA1192675X
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 宇宙航空研究開発機構 : 宇宙航空研究開発機構リポジトリ