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Topics in the Detection of Gravitational Waves from Compact Binary Inspirals.

机译：从紧凑型二元螺旋形探测引力波的主题。

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摘要

Orbiting compact binaries - such as binary black holes, binary neutron stars and neutron star-black hole binaries - are among the most promising sources of gravitational waves observable by ground-based interferometric detectors. Despite numerous sophisticated engineering techniques, the gravitational wave signals will be buried deep within noise generated by various instrumental and environmental processes, and need to be extracted via a signal processing technique referred to as matched filtering.;Matched filtering requires large banks of signal templates that are faithful representations of the true gravitational waveforms produced by astrophysical binaries. The accurate and efficient production of templates is thus crucial to the success of signal processing and data analysis. To that end, the dissertation presents a numerical technique that calibrates existing analytical (Post-Newtonian) waveforms, which are relatively inexpensive, to more accurate fiducial waveforms that are computationally expensive to generate. The resulting waveform family is significantly more accurate than the analytical waveforms, without incurring additional computational costs of production.;Certain kinds of transient background noise artefacts, called "glitches'', can masquerade as gravitational wave signals for short durations and throw-off the matched-filter algorithm. Identifying glitches from true gravitational wave signals is a highly non-trivial exercise in data analysis which has been attempted with varying degrees of success. We present here a machine-learning based approach that exploits the various attributes of glitches and signals within detector data to provide a classification scheme that is a significant improvement over previous methods.;The dissertation concludes by investigating the possibility of detecting a non-linear DC imprint, called the Christodoulou memory, produced in the arms of ground-based interferometers by the recently detected gravitational waves. The memory, which is even smaller in amplitude than the primary (detected) gravitational waves, will almost certainly not be seen in the current detection event. Nevertheless, future space-based detectors will likely be sensitive enough to observe the memory.
机译：轨道紧凑的双星（例如双星黑洞，双星中子星和中子星-黑洞双星）是地面干涉仪可观测到的最有希望的引力波源之一。尽管有许多复杂的工程技术，重力波信号将被深埋在各种仪器和环境过程产生的噪声中，并且需要通过称为匹配滤波的信号处理技术来提取;匹配滤波需要大量的信号模板库是天体双星产生的真实引力波形的忠实表示。因此，模板的准确和高效生产对于信号处理和数据分析的成功至关重要。为此，本文提出了一种数值技术，该技术将相对便宜的现有分析（牛顿后）波形校准为更精确的基准波形，这些基准波形产生的计算量很大。所产生的波形系列比分析波形要精确得多，而不会产生额外的生产成本。某些瞬态背景噪声伪像（称为“毛刺”）可以在短时间内伪装成重力波信号并甩掉匹配滤波器算法。从真实引力波信号中识别毛刺是数据分析中非常重要的练习，尝试了不同程度的成功，在此我们介绍一种基于机器学习的方法，该方法利用了毛刺和信号的各种属性在探测器数据中提供一个分类方案，这是对以前方法的重大改进。论文的结论是研究了探测由地面干涉仪产生的非线性DC印记（称为Christodoulou记忆）的可能性。最近检测到的重力波。记忆，甚至更小比主（检测到的）引力波的纬度要高，几乎可以肯定在当前的检测事件中看不到。但是，未来的天基探测器可能足够敏感以观察内存。

著录项

  • 作者

    Kapadia, Shasvath Jagat.;

  • 作者单位

    University of Arkansas.;

  • 授予单位 University of Arkansas.;
  • 学科 Physics.;Astrophysics.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 204 p.
  • 总页数 204
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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