A Trifecta of Modelling Tools: A Bayesian Binary Black Hole Model Selection combining Population Synthesis and Galaxy Formation Models

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Rauf, Liana, Howlett, Cullan, Stevenson, Simon, Riley, Jeff, Willcox, Reinhold
Format: Preprint
Published: 2024
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866910655477448704
author Rauf, Liana
Howlett, Cullan
Stevenson, Simon
Riley, Jeff
Willcox, Reinhold
author_facet Rauf, Liana
Howlett, Cullan
Stevenson, Simon
Riley, Jeff
Willcox, Reinhold
contents Gravitational waves (GWs) have revealed surprising properties of binary black hole (BBH) populations, but there is still mystery surrounding how these compact objects evolve. We apply Bayesian inference and an efficient method to calculate the BBH merger rates in the Shark host galaxies, to determine the combination of COMPAS parameters that outputs a population most like the GW sources from the LVK transient catalogue. For our COMPAS models, we calculate the likelihood with and without the dependence on the predicted number of BBH merger events. We find strong correlations between hyper-parameters governing the specific angular momentum (AM) of mass lost during mass transfer, the mass-loss rates of Wolf-Rayet stars via winds and the chemically homogeneous evolution (CHE) formation channel. We conclude that analysing the marginalised and unmarginalised likelihood is a good indicator of whether the population parameters distribution and number of observed events reflect the LVK data. In doing so, we see that the majority of the models preferred in terms of the population-level parameters of the BBHs greatly overpredict the number of events we should have observed to date. Looking at the smaller number of models which perform well with both likelihoods, we find that those with no CHE, AM loss occurring closer to the donor during the first mass-transfer event, and/or higher rates of mass-loss from Wolf-Rayet winds are generally preferred by current data. We find these conclusions to be robust to our choice of selection criteria.
format Preprint
id arxiv_https___arxiv_org_abs_2406_11885
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A Trifecta of Modelling Tools: A Bayesian Binary Black Hole Model Selection combining Population Synthesis and Galaxy Formation Models
Rauf, Liana
Howlett, Cullan
Stevenson, Simon
Riley, Jeff
Willcox, Reinhold
High Energy Astrophysical Phenomena
Cosmology and Nongalactic Astrophysics
General Relativity and Quantum Cosmology
Gravitational waves (GWs) have revealed surprising properties of binary black hole (BBH) populations, but there is still mystery surrounding how these compact objects evolve. We apply Bayesian inference and an efficient method to calculate the BBH merger rates in the Shark host galaxies, to determine the combination of COMPAS parameters that outputs a population most like the GW sources from the LVK transient catalogue. For our COMPAS models, we calculate the likelihood with and without the dependence on the predicted number of BBH merger events. We find strong correlations between hyper-parameters governing the specific angular momentum (AM) of mass lost during mass transfer, the mass-loss rates of Wolf-Rayet stars via winds and the chemically homogeneous evolution (CHE) formation channel. We conclude that analysing the marginalised and unmarginalised likelihood is a good indicator of whether the population parameters distribution and number of observed events reflect the LVK data. In doing so, we see that the majority of the models preferred in terms of the population-level parameters of the BBHs greatly overpredict the number of events we should have observed to date. Looking at the smaller number of models which perform well with both likelihoods, we find that those with no CHE, AM loss occurring closer to the donor during the first mass-transfer event, and/or higher rates of mass-loss from Wolf-Rayet winds are generally preferred by current data. We find these conclusions to be robust to our choice of selection criteria.
title A Trifecta of Modelling Tools: A Bayesian Binary Black Hole Model Selection combining Population Synthesis and Galaxy Formation Models
topic High Energy Astrophysical Phenomena
Cosmology and Nongalactic Astrophysics
General Relativity and Quantum Cosmology
url https://arxiv.org/abs/2406.11885