The Laser Interferometer Space Antenna (LISA) is expected to detect thousands of individually resolved gravitational wave sources, overlapping in time and frequency, on top of unresolved astrophysical and/or primordial backgrounds. Disentangling resolved sources from backgrounds and extracting their parameters in a computationally intensive "global fit" is normally regarded as a necessary step toward reconstructing the properties of the underlying astrophysical populations. Here, we show that it is in principle feasible to infer the population properties of the most numerous of LISA sources -- Galactic double white dwarfs -- directly from the frequency (or, equivalently, time) strain series by adopting a simulation-based approach, without extracting and estimating the parameters of each single source. By training a normalizing flow on a custom-designed compression of simulated LISA frequency series from the Galactic double white dwarf population, we demonstrate how to infer the posterior distribution of population parameters (e.g., mass function, frequency, and spatial distributions). This allows for extracting information on the population parameters from both resolved and unresolved sources simultaneously and in a computationally efficient manner. This approach can be extended to other source classes (e.g., massive and stellar-mass black holes, extreme mass ratio inspirals) and to scenarios involving non-Gaussian or non-stationary noise (e.g., data gaps), provided that fast and accurate simulations are available.

翻译：激光干涉空间天线（LISA）预计将探测到数千个在时间和频率上重叠的独立可分辨引力波源，这些信号叠加在未分辨的天体物理和/或原初背景之上。通常认为，通过计算密集型的"全局拟合"将可分辨源从背景中分离并提取其参数，是重建潜在天体物理群体特性的必要步骤。本文表明，通过采用基于模拟的方法，原则上可以直接从频率（或等效地，时间）应变序列中推断LISA数量最多的源——银河双白矮星——的群体特性，而无需提取和估计每个单独源的参数。通过在模拟的银河双白矮星群体LISA频率序列的自定义压缩表示上训练归一化流，我们展示了如何推断群体参数（例如质量函数、频率和空间分布）的后验分布。这使得能够以计算高效的方式，同时从可分辨和未分辨的源中提取群体参数信息。该方法可扩展至其他源类别（例如大质量与恒星级黑洞、极端质量比旋近），以及涉及非高斯或非平稳噪声（例如数据间隙）的场景，前提是具备快速且准确的模拟。