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

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.