发表机构
University of California, Irvine; Stony Brook University; California Institute of Technology（加州大学尔湾分校; 石溪大学; 加州理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文评估LISA探测随机引力波记忆背景的前景，发现其频谱远小于先前估计且呈非高斯爆米花特征，并强调结果对种群模型的高度依赖性。
AI 中文摘要
引力波（GW）记忆是由引力波爆发引起的时空几何的永久变化。由于其与渐近对称性的联系，它在理论上很有趣，但尚未被观测到。即将到来的激光干涉仪空间天线（LISA）预计将直接探测到超大质量黑洞（SMBH）并合中明亮事件产生的记忆信号。我们评估了记忆贡献的累积效应作为随机引力波记忆背景（SGWMB）的前景。使用两个SMBH种群模型，我们恢复了先前获得的单事件信噪比预测，并使用数值相对论波形对每次事件中的记忆信号进行建模，计算了未解析背景的功率谱。我们发现预测的频谱显著小于使用Heaviside函数建模记忆波形所得的频谱，预期信噪比在$O(0.1-10)$量级。我们进一步发现，我们的模型倾向于高度非高斯（“爆米花”）频谱，表明该背景可能不是连续、重叠信号的集合，而是间歇性爆发的集合。在这两种度量中，我们证明了当前对频谱的任何预测都高度依赖于种群模型的选择。我们结合未来LISA任务对这些发现进行解读，并强调其在LISA全局拟合中正确处理记忆信号的重要性。
英文摘要
Gravitational-wave (GW) memory is a permanent change in spacetime geometry induced by a burst of gravitational waves. It is theoretically interesting for its connection to asymptotic symmetries, but has not yet been observed. The upcoming Laser Interferometer Space Antenna (LISA) is predicted to detect memory directly in loud events from supermassive black hole (SMBH) mergers. We assess the prospects for the cumulative effect of memory contributions as a stochastic GW memory background (SGWMB). Using two SMBH population models, we recover previously obtained single-event SNR forecasts and compute the power spectrum of the unresolved background, using numerical-relativity waveforms for modeling the memory signal during each event. We find the predicted spectrum is significantly smaller than ones found by modeling the memory waveform with a Heaviside function, with expected SNRs of order $O(0.1-10)$. We further find that our models favor a highly non-Gaussian ("popcorn") spectrum, demonstrating that the background may not appear as the union of continuous, overlapping signals, but rather as a collection of intermittent bursts. In both measures, we demonstrate that any current prediction of the spectrum is heavily dependent on the choice of population model. We interpret these findings in the light of the future LISA mission and emphasize their importance for properly handling memory within the LISA global fit.
Comments14 pages, 10 figures, submitted to Physical Review D
