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Constraining the r-mode saturation amplitude from a hypothetical detection of r-mode gravitational waves from a newborn neutron star: Detection strategies and machine learning algorithms.

机译:通过假设检测来自新生中子星的r模式引力波来约束r模式饱和幅度:检测策略和机器学习算法。

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摘要

Newborn neutron stars have been thought of as very promising sources of detectable gravitational waves since the discovery (1998) that r-mode oscillations of neutron stars are driven unstable by gravitational radiation. Important factors that increase the likelihood for such a detection are: high rotational velocities of newborn neutron stars, high saturation amplitudes of the r-mode oscillations and also validity of the r-mode gravitational radiation theories that claim to have explained the spin down of newborn neutron stars to the observed rotational velocities. My present work is focused on the experimental and computational part for the r-mode gravitational wave detection as well as the physical implications from such a detection. My contribution in the field consists of the following topics: (a) derive an expression for the moment of inertia (MOI) of neutron stars as a function of observables from a hypothetical r-mode gravitational wave detection, (b) demonstrate that by constraining the MOI of a neutron star from a hypothetical r-mode detection, we can set constraints on the equation of state of the matter of the neutron star, (c) for each candidate equation of state, present a method that sets an upper bound on the saturation amplitude of the r-mode oscillations, (d) perform a sensitivity study on the existing clustering algorithms using the stochastic pipeline of the LIGO collaboration and (e) demonstrate the validity of machine learning algorithms as decision making algorithms whose performance is at least as good as that of the existing clustering algorithms.
机译:自从发现(1998年)中子星的r模振荡是由引力辐射驱动而不稳定以来,新生中子星就被认为是非常有前途的可探测引力波来源。增加进行这种探测的可能性的重要因素是:新生中子星的高旋转速度,r型振荡的高饱和振幅以及声称解释了新生儿旋转的r模式引力辐射理论的有效性中子星向观测到的旋转速度移动。我目前的工作集中在r型重力波检测的实验和计算部分以及这种检测的物理意义上。我在该领域的贡献包括以下主题:(a)从假设的r模式重力波检测中得出中子星的惯性矩(MOI)随可观测物的变化的表达式,(b)通过约束证明通过假设的r模式检测得到的中子星的MOI,我们可以对中子星的状态方程设置约束,(c)为每个候选状态方程,提出一种在(d)使用LIGO合作的随机流水线对现有聚类算法进行敏感性研究,并且(e)证明机器学习算法作为性能至少为和现有的聚类算法一样好。

著录项

  • 作者

    Mitidis, Andonis.;

  • 作者单位

    University of Florida.;

  • 授予单位 University of Florida.;
  • 学科 Computer engineering.;Astrophysics.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 151 p.
  • 总页数 151
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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