Introduction

CHIMERA is a Python package for hierarchical Bayesian inference of gravitational wave population parameters using galaxy catalog data. It extends the framework from Mandel et al. 2019 and Vitale et al. 2022.

See also

More details of the statistical framework are presented in Borghi et al. 2024, for most recent implementations (performance and GPU support) see Tagliazucchi et al. 2025.

Framework and Code Structure

CHIMERA evaluates the hyper-likelihood for population parameters \(\boldsymbol{\lambda}=\{\boldsymbol{\lambda}_\mathrm{c},\boldsymbol{\lambda}_\mathrm{m},\boldsymbol{\lambda}_\mathrm{z}\}\) (cosmology, source mass distribution, rate evolution):

\[p(\boldsymbol{d}^{\rm GW} | \boldsymbol{\lambda}) \propto \frac{1}{\xi(\boldsymbol{\lambda})^{N_{\rm ev}}} \prod_{i=1}^{N_{\rm ev}} \int \mathcal{K}_{\mathrm{gw},i}(z, \hat{\Omega} | \boldsymbol{\lambda}_\mathrm{c}, \boldsymbol{\lambda}_\mathrm{m}) \, p_{\rm gal}(z, \hat{\Omega} | \boldsymbol{\lambda}_{\rm c})\, \frac{\psi(z ; \boldsymbol{\lambda}_{\rm z})}{1+z}\, \mathrm{d}z\, \mathrm{d}\hat{\Omega}\]

The GW kernel \(\mathcal{K}_{\mathrm{gw},i}\) is computed via KDE while the selection bias \(\xi(\boldsymbol{\lambda})\) uses Monte Carlo integration.

Core modules:

• likelihood.py - Main likelihood computation

• selection_function.py - Selection bias calculations

• data.py - GW and electromagnetic data handling

• population/ - Population models (mass, rate, cosmology)

• catalog/ - Galaxy catalog processing for redshift priors

Structure and dependencies: