Revealing massive black hole astrophysics: The potential of hierarchical inference with extreme mass-ratio inspiral observations

TL;DR

This paper evaluates how hierarchical Bayesian inference applied to simulated EMRI gravitational wave data from LISA can constrain massive black hole populations, revealing the method's strengths and limitations in astrophysical parameter estimation.

Contribution

It introduces a hierarchical inference framework for EMRI populations, demonstrating its effectiveness and robustness in constraining black hole demographics from simulated LISA data.

Findings

Population features with sharp distributions can be tightly constrained.

Disentangling mixed populations is possible with around 20 detections.

Inference remains sensitive to key features even with model misspecification.

Abstract

Gravitational waves from extreme mass-ratio inspirals (EMRIs) will enable sub-percent measurements of massive black hole parameters and provide access to the demographics of compact objects in galactic nuclei. During the LISA mission, multiple EMRIs are expected to be detected, allowing statistical studies of massive black hole populations and their formation pathways. We perform hierarchical Bayesian inference on simulated EMRI catalogues to assess how well LISA could constrain the astrophysical population using parametrised population models. We test our inference framework on a variety of populations, including heterogeneous and homogeneous mixtures of parametrised subpopulations, and scenarios in which the assumed model is deliberately misspecified. Our results show that population parameters governing distributions with sharp features can be tightly constrained. Mixed populations can be disentangled with as few as $\sim20$ detections, and even with model misspecification, the inference retains sensitivity to key population features. These results demonstrate the capabilities and limitations of EMRI population inference, providing guidance for constructing realistic astrophysical population models for LISA analysis.