ICAROGW: A python package for inference of astrophysical population properties of noisy, heterogeneous and incomplete observations

TL;DR

ICAROGW 2.0 is a Python tool designed for inferring astrophysical and cosmological properties from noisy, heterogeneous gravitational wave data, enabling efficient analysis of large GW datasets and integration with galaxy surveys and electromagnetic observations.

Contribution

The paper introduces ICAROGW 2.0, a new version of a Python package that improves computational efficiency and versatility for population and cosmological inference from GW data.

Findings

GPU acceleration increases analysis speed by two orders of magnitude.

The code accurately reproduces previous GW population and cosmological results.

ICAROGW 2.0 supports multiple data sources, including GWs, galaxy surveys, and electromagnetic counterparts.

Abstract

We present icarogw 2.0, a pure CPU/GPU python code developed to infer astrophysical and cosmological population properties of noisy, heterogeneous, and incomplete observations. icarogw 2.0 is mainly developed for compact binary coalescence (CBC) population inference with gravitational wave (GW) observations. The code contains several models for masses, spins, and redshift of CBC distributions, and is able to infer population distributions as well as the cosmological parameters and possible general relativity deviations at cosmological scales. We present the theoretical and computational foundations of icarogw 2.0, and we describe how the code can be employed for population and cosmological inference using (i) only GWs, (ii) GWs and galaxy surveys and (iii) GWs with electromagnetic counterparts. We discuss the code performance on Graphical Processing Units (GPUs), finding a gain in computation time of about two orders of magnitudes when more than 100 GW events are involved for the analysis. We validate the code by re-analyzing GW population and cosmological studies, finding very good agreement with previous publications.