Clustering of binary black hole mergers: a detailed analysis of the EAGLE+MOBSE simulation

TL;DR

This paper investigates the cosmological bias of gravitational wave events from binary black hole mergers using simulations and compares different models, ultimately training a neural network to understand key host galaxy parameters influencing BBH merger distributions.

Contribution

It introduces a detailed analysis combining hydrodynamical simulations, semi-analytical models, and neural networks to study BBH merger distributions and their dependence on host galaxy properties.

Findings

Overall agreement between simulation and HOD models when properties match.

Discrepancies highlight the need for more robust models.

Neural network identifies key host galaxy parameters affecting BBH distribution.

Abstract

We perform a detailed study of the cosmological bias of gravitational gave (GW) events produced by binary black hole mergers (BBHM). We start from a BBHM distribution modeled inside the EAGLE hydrodyamical simulation using the population synthesis code MOBSE. We then compare our findings with predictions from different Halo Occupation Distribution (HOD) prescriptions and find overall agreement, provided that the modeled properties of host galaxies and halos in the semi-analytical treatment match those in the simulations. By highlighting the sources of these discrepancies, we provide the stepping stone to build future more robust models that prevent the shortcoming of both simulation-based and analytical models. Finally, we train a neural network to build a simulation-based HOD and perform feature importance analysis to gain intuition on which host halo/galaxy parameters are the most relevant in determining the actual distribution and power spectrum of BBHM. We find that the distribution of BBHM in a galaxy does not only depend on its size, star formation rate and metallicity, but also by its kinetic state.