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 possible to infer the properties of the most numerous population of LISA sources - Galactic double white dwarfs - directly from the frequency (or, equivalently, time) strain series, by using a simulation-based approach that bypasses the global fit entirely. 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. Our approach to target population properties directly can be readily extended to other source classes (e.g., massive and stellar-mass black holes, extreme mass ratio inspirals), provided fast simulations are available, and to scenarios involving non-Gaussian or non-stationary noise (e.g., data gaps).

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。传统上，通过计算密集的"全局拟合"将已分辨源从背景中分离并提取其参数，通常被视为重建底层天体物理群体特性的必要步骤。本文证明，通过采用完全绕过全局拟合的模拟方法，可以直接从频率（或等效时间）应变序列中推断LISA数量最多的源群体——银河双白矮星——的特性。通过训练归一化流对模拟的银河双白矮星群体LISA频率序列进行定制化压缩，我们展示了如何推断群体参数（如质量函数、频率和空间分布）的后验分布。这种方法能够同时以计算高效的方式从已分辨和未分辨源中提取群体参数信息。我们这种直接获取群体特性的方法可轻松扩展至其他源类别（如大质量与恒星级黑洞、极端质量比旋进），前提是具备快速模拟能力，并可适用于涉及非高斯或非平稳噪声（如数据间隙）的场景。