Discriminating between different scenarios for the formation and evolution of massive black holes with LISA

TL;DR

This paper presents a hierarchical Bayesian inference pipeline to determine the dominant formation scenarios of massive black holes using LISA gravitational wave data, effectively distinguishing between models despite measurement uncertainties.

Contribution

It introduces a novel Bayesian inference method to compare theoretical models of black hole formation with simulated LISA data, enabling accurate population property inference.

Findings

The pipeline accurately infers black hole population properties from simulated data.

Measurement errors have minimal impact on the inference accuracy.

The method reliably distinguishes between different formation scenarios.

Abstract

Electromagnetic observations have provided strong evidence for the existence of massive black holes in the center of galaxies, but their origin is still poorly known. Different scenarios for the formation and evolution of massive black holes lead to different predictions for their properties and merger rates. LISA observations of coalescing massive black hole binaries could be used to reverse engineer the problem and shed light on these mechanisms. In this paper, we introduce a pipeline based on hierarchical Bayesian inference to infer the mixing fraction between different theoretical models by comparing them to LISA observations of massive black hole mergers. By testing this pipeline against simulated LISA data, we show that it allows us to accurately infer the properties of the massive black hole population as long as our theoretical models provide a reliable description of the Universe. We also show that measurement errors, including both instrumental noise and weak lensing errors, have little impact on the inference.