Massive Black Hole Binary Inspirals: Results from the LISA Parameter Estimation Taskforce

TL;DR

This paper evaluates LISA's ability to detect and localize massive black hole binaries across four different population models, using multiple codes to ensure reliable parameter estimation results.

Contribution

It compares four independent Fisher-matrix based codes for LISA parameter estimation, demonstrating their agreement and applying them to diverse black hole binary population models.

Findings

High consistency among the four codes after standardization.

Estimated detection rates vary across models based on seed size and accretion efficiency.

Inclusion of spin precession and higher harmonics improves parameter estimation accuracy.

Abstract

The LISA Parameter Estimation (LISAPE) Taskforce was formed in September 2007 to provide the LISA Project with vetted codes, source distribution models, and results related to parameter estimation. The Taskforce's goal is to be able to quickly calculate the impact of any mission design changes on LISA's science capabilities, based on reasonable estimates of the distribution of astrophysical sources in the universe. This paper describes our Taskforce's work on massive black-hole binaries (MBHBs). Given present uncertainties in the formation history of MBHBs, we adopt four different population models, based on (i) whether the initial black-hole seeds are small or large, and (ii) whether accretion is efficient or inefficient at spinning up the holes. We compare four largely independent codes for calculating LISA's parameter-estimation capabilities. All codes are based on the Fisher-matrix approximation, but in the past they used somewhat different signal models, source parametrizations and noise curves. We show that once these differences are removed, the four codes give results in extremely close agreement with each other. Using a code that includes both spin precession and higher harmonics in the gravitational-wave signal, we carry out Monte Carlo simulations and determine the number of events that can be detected and accurately localized in our four population models.