论文标题

在通货膨胀期间,从尖锐特征和颗粒产生的随机重力波背景中的振荡背景

Oscillations in the stochastic gravitational wave background from sharp features and particle production during inflation

论文作者

Fumagalli, Jacopo, Renaux-Petel, Sébastien, Witkowski, Lukas T.

论文摘要

我们在膨胀过程中粒子产生的标量诱导的随机重力波背景中确定了一种特征模式。如果粒子产生足够高效,则标量功率谱将表现出$ \ Mathcal {O}(1)$振荡定期为$ k $,具有尖锐特征的特征,具有指数增强的信封。我们系统地研究了通货膨胀后产生的引力波诱导的引力波的性能,并发现这将周期性结构固定在$ K $中,从而带有$ \ Mathcal {o}(O}(O}(O}(10 \%)),引力波能量谱峰达到峰值。标量功率谱中振荡的频率取决于通货膨胀期间特征的规模,进而确定了重力波信号中调制的频率。我们在多场通货膨胀的框架中以强烈的尖锐转弯在通货膨胀轨迹中表达了这种现象的明确实现。在未来的引力波观测站中可能检测到所得的随机背景,并且可以使用对反应和扰动性的考虑来从理论方面约束参数空间。我们的工作激发了更广泛的研究,将原始特征与重力波的随机背景的可观察性质联系起来,并在数据分析中进行了专门的开发以进行检测。

We identify a characteristic pattern in the scalar-induced stochastic gravitational wave background from particle production during inflation. If particle production is sufficiently efficient, the scalar power spectrum exhibits $\mathcal{O}(1)$ oscillations periodic in $k$, characteristic of a sharp feature, with an exponentially enhanced envelope. We systematically study the properties of the induced spectrum of gravitational waves sourced after inflation and find that this inherits the periodic structure in $k$, resulting in a peak in the gravitational wave energy density spectrum with $\mathcal{O}(10 \%)$ modulations. The frequency of the oscillation in the scalar power spectrum is determined by the scale of the feature during inflation and in turn sets the frequency of modulations in the gravitational wave signal. We present an explicit realisation of this phenomenon in the framework of multifield inflation, in the form of a strong sharp turn in the inflationary trajectory. The resulting stochastic background is potentially detectable in future gravitational wave observatories, and considerations of backreaction and perturbativity can be used to constrain the parameter space from the theoretical side. Our work motivates more extensive research linking primordial features to observable properties of the stochastic background of gravitational waves, and dedicated development in data analysis for their detection.

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