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The Search for Gravitational Waves from the Coalescence of Black Hole Binary Systems in Data from the LIGO and Virgo Detectors Or: A Dark Walk through a Random Forest.

机译:从LIGO和处女座探测器的数据中黑洞二元系统的合并中寻找引力波,或者:穿过随机森林的黑暗漫步。

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摘要

The LIGO and Virgo gravitational-wave observatories are complex and extremely sensitive strain detectors that can be used to search for a wide variety of gravitational waves from astrophysical and cosmological sources. In this thesis, I motivate the search for the gravitational wave signals from coalescing black hole binary systems with total mass between 25 and 100 solar masses. The mechanisms for formation of such systems are not well-understood, and we do not have many observational constraints on the parameters that guide the formation scenarios. Detection of gravitational waves from such systems---or, in the absence of detection, the tightening of upper limits on the rate of such coalescences---will provide valuable information that can inform the astrophysics of the formation of these systems. I review the search for these systems and place upper limits on the rate of black hole binary coalescences with total mass between 25 and 100 solar masses. I then show how the sensitivity of this search can be improved by up to 40% by the the application of the multivariate statistical classifier known as a random forest of bagged decision trees to more effectively discriminate between signal and non-Gaussian instrumental noise. I also discuss the use of this classifier in the search for the ringdown signal from the merger of two black holes with total mass between 50 and 450 solar masses and present upper limits. I also apply multivariate statistical classifiers to the problem of quantifying the non-Gaussianity of LIGO data. Despite these improvements, no gravitational-wave signals have been detected in LIGO data so far. However, the use of multivariate statistical classification can significantly improve the sensitivity of the Advanced LIGO detectors to such signals.
机译:LIGO和处女座引力波观测器是复杂且极其灵敏的应变检测器,可用于从天体物理学和宇宙学源中搜索各种各样的引力波。在本文中,我激发了从聚结黑洞二元系统(总质量在25到100太阳质量之间)中寻找引力波信号的动机。此类系统的形成机制尚不为人所理解,并且对于指导形成方案的参数,我们没有太多观察上的约束。对来自此类系统的引力波的检测-或在没有检测到的情况下,对此类合并速率的上限进行加紧-将提供有价值的信息,这些信息可为这些系统的形成向天体物理学提供信息。我回顾了对这些系统的搜索,并对总质量在25到100太阳质量之间的黑洞二元合并率设置了上限。然后,我展示了如何通过应用称为袋装决策树的随机森林的多元统计分类器来更有效地区分信号噪声和非高斯工具噪声,将搜索的灵敏度提高40%。我还讨论了使用此分类器来搜索两个黑洞合并后的振铃信号,黑洞的总质量在50到450太阳质量之间,并且存在上限。我还将多元统计分类器应用于量化LIGO数据的非高斯性的问题。尽管有了这些改进,到目前为止,LIGO数据中仍未检测到重力波信号。但是,使用多元统计分类可以显着提高Advanced LIGO检测器对此类信号的敏感性。

著录项

  • 作者

    Hodge, Kari Alison.;

  • 作者单位

    California Institute of Technology.;

  • 授予单位 California Institute of Technology.;
  • 学科 Astronomy.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 235 p.
  • 总页数 235
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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