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On the separation of T Tauri star spectra using non-negative matrix factorization and Bayesian positive source separation.

机译：关于使用非负矩阵分解和贝叶斯正源分离的T Tauri星光谱的分离。

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摘要

The objective of this study is to compare and evaluate Bayesian and deterministic methods of positive source separation of young star spectra. In the Bayesian approach, the proposed Bayesian Positive Source Separation (BPSS) method uses Gamma priors to enforce non-negativity in the source signals and mixing coefficients and a Markov Chain Monte Carlo (MCMC) algorithm, modified by suggesting simpler proposal distributions and randomly initializing the MCMC to correctly separate spectra. In the deterministic approach, two Non-negative Matrix Factorization (NNMF) algorithms, the multiplicative update rule algorithm and an alternating least squares algorithm, are used to separate the star spectra into sources. The BPSS and NNMF algorithms are applied to the field of Astrophysics by applying the source separation techniques to T Tauri star spectra, resulting in a successful decomposition of the spectra into their sources. These methods are for the first time being applied and evaluated in optical spectroscopy. The results show that, while both methods perform well, BPSS outperforms NNMF. The NNMF and BPSS algorithms improve upon the current methodology used in Astrophysics iu two important ways. First, they permit the identification of additional components of the spectra in addition to the photosphere and boundary layer which can be modeled with current methods. Second, by applying a statistical algorithm, the modeling of T Tauri stars becomes less subjective. These methods may be further extrapolated to model spectra from other types of stars or astrophysical phenomena.
机译：这项研究的目的是比较和评估年轻恒星光谱正源分离的贝叶斯方法和确定性方法。在贝叶斯方法中，建议的贝叶斯正源分离（BPSS）方法使用Gamma先验来增强源信号和混合系数中的非负性，并采用马尔可夫链蒙特卡洛（MCMC）算法，通过建议更简单的提议分布和随机初始化对其进行了修改MCMC以正确分离光谱。在确定性方法中，使用两种非负矩阵分解（NNMF）算法，即乘性更新规则算法和交替最小二乘算法，将星图谱分离为多个源。通过将源分离技术应用于T Tauri星光谱，将BPSS和NNMF算法应用于天体物理学领域，从而成功地将光谱分解成其源。这些方法是首次在光谱学中应用和评估。结果表明，尽管两种方法都表现良好，但BPSS优于NNMF。 NNMF和BPSS算法以两种重要方式改进了目前在天体物理学中使用的方法。首先，它们允许识别可以用当前方法建模的除光球和边界层之外的光谱的其他分量。其次，通过应用统计算法，T Tauri星的建模变得较不主观。这些方法可以进一步推论为其他类型的恒星或天体物理学现象的光谱建模。

著录项

  • 作者

    Kenney, Colleen.;

  • 作者单位

    Rice University.;

  • 授予单位 Rice University.;
  • 学科 Statistics.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 40 p.
  • 总页数 40
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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