# Probing Massive Black Hole Binary Populations with LISA

## TL;DR

This study uses cosmological simulations to predict LISA's detection rates of merging massive black hole binaries, analyzing how different evolutionary models influence these rates and properties.

## Contribution

It introduces a novel analysis of LISA detection prospects using Illustris simulation data and compares binary evolution models affecting detection predictions.

## Key findings

- LISA detection rates of ~0.5-1 per year for black holes >10^5 M_sun
- Detection rates are likely underestimated due to mass range limitations
- Different binary evolution models significantly impact detection predictions

## Abstract

ESA and NASA are moving forward with plans to launch LISA around 2034. With data from the Illustris cosmological simulation, we provide analysis of LISA detection rates accompanied by characterization of the merging massive black hole population. Massive black holes of total mass $\sim10^5-10^{10} M_\odot$ are the focus of this study. We evolve Illustris massive black hole mergers, which form at separations on the order of the simulation resolution ($\sim$kpc scales), through coalescence with two different treatments for the binary massive black hole evolutionary process. The coalescence times of the population, as well as physical properties of the black holes, form a statistical basis for each evolutionary treatment. From these bases, we Monte Carlo synthesize many realizations of the merging massive black hole population to build mock LISA detection catalogs. We analyze how our massive black hole binary evolutionary models affect detection rates and the associated parameter distributions measured by LISA. With our models, we find massive black hole binary detection rates with LISA of $\sim0.5-1$ yr$^{-1}$ for massive black holes with masses greater than $10^5M_\odot$. This should be treated as a lower limit primarily because our massive black hole sample does not include masses below $10^5M_\odot$, which may significantly add to the observed rate. We suggest reasons why we predict lower detection rates compared to much of the literature.