标题： 用LISA探测随机引力波背景 显示英文标题

标题： Uncovering stochastic gravitational-wave backgrounds with LISA

摘要： 寻找天体物理或原初起源的随机引力波背景（SGWB）是当前和未来引力波观测站的一项重要任务。 虽然像LIGO-Virgo-Kagra或脉冲星计时阵列这样的探测器网络可以利用交叉相关来区分仪器噪声和SGWB，但LISA可能是在2035年唯一运行的同类探测器。 这种独特性对数据分析提出了挑战。 为了解决这个问题，我们提出了一种基于贝叶斯模型选择的策略。 我们使用了一个灵活的噪声功率谱密度~(PSD)模型，并利用噪声和信号传递函数的知识，以便在噪声PSD未知的情况下检测SGWB。 通过这种技术，我们随后探索了LISA可访问的幂律SGWB形状的参数空间。 显示英文摘要

摘要： Finding a stochastic gravitational-wave background (SGWB) of astrophysical or primordial origin is one of the quests of current and future gravitational-wave observatories. While detector networks such as LIGO-Virgo-Kagra or pulsar timing arrays can use cross-correlations to tell instrumental noise and SGWB apart, LISA is likely to be the only flying detector of its kind in 2035. This particularity poses a challenge for data analysis. To tackle it, we present a strategy based on Bayesian model selection. We use a flexible noise power spectral density~(PSD) model and the knowledge of noise and signal transfer functions to allow SGWBs detection when the noise PSD is unknown. With this technique, we then probe the parameter space accessible by LISA for power-law SGWB shapes.