Simulation-based population inference of LISA's Galactic binaries: Bypassing the global fit

2025-06-27

AI Summary

To address the computational bottleneck of global fitting in population parameter inference for double white dwarfs (DWDs) in LISA data, this work proposes a simulation-based Bayesian inference framework that bypasses global optimization entirely. The method operates directly on frequency-domain strain data, jointly modeling resolved and unresolved DWD signals. It employs a customized frequency-domain compression scheme coupled with a normalizing flow model to efficiently approximate the population posterior distribution from simulated data. Its key innovation lies in enabling end-to-end population inference without source-by-source modeling or iterative global optimization—marking the first such approach—and inherently accommodating non-Gaussian, non-stationary instrumental noise. Experiments demonstrate estimation accuracy for critical population parameters—including the DWD mass function, spatial number density, and orbital frequency distribution—that matches conventional methods, while reducing computational cost by one to two orders of magnitude.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

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).

Problem

• Bypass global fit for LISA's Galactic binaries inference
• Infer population parameters from resolved and unresolved sources
• Extend approach to other source classes and noise scenarios

Innovation

• Simulation-based approach bypasses global fit
• Normalizing flow trained on compressed LISA data
• Directly infers population parameters from strain series