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

arXiv: https://arxiv.org/abs/2506.22543 · HTML: https://mlsanity.com/paper/2506.22543/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 68 references that are in the map.
- First posted on arXiv 27 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [Masked Autoregressive Flow for Density Estimation](https://mlsanity.com/paper/1705.07057/) (2017)
- [The frontier of simulation-based inference](https://mlsanity.com/paper/1911.01429/) (2019)
- [Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows](https://mlsanity.com/paper/1805.07226/) (2018)
- [Validating Bayesian Inference Algorithms with Simulation-Based Calibration](https://mlsanity.com/paper/1804.06788/) (2018)
- [Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks](https://mlsanity.com/paper/2112.08866/) (2021)
- [Learning Robust Statistics for Simulation-based Inference under Model Misspecification](https://mlsanity.com/paper/2305.15871/) (2023)
- [Truncated Marginal Neural Ratio Estimation](https://mlsanity.com/paper/2107.01214/) (2021)
- [Addressing Misspecification in Simulation-based Inference through Data-driven Calibration](https://mlsanity.com/paper/2405.08719/) (2024)
- [Tests for model misspecification in simulation-based inference: from local distortions to global model checks](https://mlsanity.com/paper/2412.15100/) (2024)

## Built on by (most cited first, 1 of 1)

- [FUSE: FK-Steered Multi-Modal Flow Matching for Efficient Simulation-Based Posterior Estimation](https://mlsanity.com/paper/2607.05252/) (2026)

## Abstract (arXiv)

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.