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

We present ICAROGW 2.0, a pure python code developed to infer the astrophysical and cosmological population properties of noisy, heterogeneous, and incomplete observations. The code has mainly been developed for compact binary coalescence (CBC) population inference with gravitational wave (GW) observations. It contains several models for the masses, spins, and redshift of CBC distributions and it is able to infer population distributions, as well as the cosmological parameters and possible general relativity deviations at cosmological scales. Here, we present the theoretical and computational foundations of ICAROGW 2.0 and 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 GPUs, finding a gain in computation time of about two orders of magnitude when more than 100 GW events are involved in the analysis. We have validated the code by re-analyzing GW population and cosmological studies, finding very good agreement with previous results.