Gravitational wave energy spectral density properties from BPASS Galactic binary population in the Milky Way galaxy

TL;DR

This study analyzes the gravitational wave energy spectral density from Galactic binary populations in the Milky Way, using simulations and different models to predict signals relevant for the LISA mission.

Contribution

It introduces a combined simulation approach to estimate GW spectral densities from Galactic binaries and compares various functional models for these spectra.

Findings

Predicted GW signal amplitude and slope for total Galactic binaries.

Estimated GW energy spectral density at 3 mHz for different binary populations.

Highlighted the importance of accurate noise modeling for GW observations.

Abstract

We analyse the energy spectral density properties of Gravitational waves from Galactic binary populations in the~\text{mHz} band targeted by the Laser Interferometer Space Antenna mission. Our analysis is based on combining BPASS with a Milky Way analogue galaxy from the Feedback In Realistic Environment (FIRE) simulations and the GWs these populations emit. Our investigation compares different functional forms of gravitational wave (GW) ESDs, namely the single power-law, broken power-law, and single-peak models, revealing disparities within and among Galactic binary populations. We estimate the ESDs for six different Galactic binary populations and the ESD of the total Galactic binary population for LISA. Employing a single power-law model, we predict a total Galactic binary GW signal amplitude $\alpha$ = $2.0^{+0.2}_{-0.2} \times 10^{-8}$ and a slope $\beta$ = $-2.64 ^{+0.03}_{-0.04}$ and the ESD $\rm h^2 \Omega_{GW}$ = $1.1 ^{+0.1}_{-0.1} \times 10^{-9}$ at 3~\text{mHz}. For the Galactic WDB binary GW signal $\alpha = 1^{+0.02}_{-0.02} \times 10^{-10}$, $\beta = -1.56 ^{+0.03}_{-0.03}$ and $\rm h^2 \Omega_{GW} = 18 ^{+1}_{-1} \times 10^{-12}$. Our analysis underscores the importance of accurate noise parameter estimation and highlights the complexities of modelling realistic observations, prompting future exploration into more flexible models.