利用未来引力波探测器从超大质量中子星推断惯性模的前景
Prospects for the inference of inertial modes from hypermassive neutron stars with future gravitational-wave detectors
AI总结：
本研究评估双中子星并合残骸惯性模的引力波可探测性，通过将数值波形注入不同探测器噪声，用BayesWave算法分析，发现当前探测器仅能探测1 Mpc内的信号，未来探测器探测范围可提升约10倍。
摘要：
近期针对双中子星并合的一些长期数值模拟表明，此类并合产生的长寿命残骸可能受对流不稳定性影响。这些不稳定性会激发惯性模，为我们通过分析并合后晚期引力波信号中惯性模的印记，增进对中子星旋转与热特性的理解提供了潜在途径。本文通过将数值生成的并合后波形注入第二代及未来探测器的有色高斯噪声中，评估了这些模的可探测性。我们使用BayesWave（一种贝叶斯数据分析算法，通过一系列正弦-高斯小波以不依赖波形形态的方法重构信号）来恢复信号。研究表明，当前的干涉仪（即Hanford-Livingston-Virgo网络）仅在并合发生在最远1 Mpc距离处时，才能恢复惯性模的峰值频率。对于像Einstein Telescope这样的未来探测器，探测范围将增加约10倍。
英文摘要：
Some recent, long-term numerical simulations of binary neutron star mergers have shown that the long-lived remnants produced in such mergers might be affected by convective instabilities. Those would trigger the excitation of inertial modes, providing a potential method to improve our understanding of the rotational and thermal properties of neutron stars through the analysis of the modes' imprint in the late post-merger gravitational-wave signal. In this paper we assess the detectability of those modes by injecting numerically generated post-merger waveforms into colored Gaussian noise of second-generation and future detectors. Signals are recovered using BayesWave, a Bayesian data-analysis algorithm that reconstructs them through a morphology-independent approach using series of sine-Gaussian wavelets. Our study reveals that current interferometers (i.e. the Handford-Livingston-Virgo network) recover the peak frequency of inertial modes only if the merger occurs at distances of up to 1 Mpc. For future detectors such as the Einstein Telescope, the range of detection increases by about a factor 10.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
