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

¹ INFN, Sezione di Roma, 00185 Roma, Italy
e-mail: mastrosi@roma1.infn.it
² Université Lyon, Université Claude Bernard Lyon 1, CNRS, IP2I Lyon/IN2P3, UMR 5822, 69622 Villeurbanne, France
³ Laboratoire des 2 Infinis – Toulouse (L2IT-IN2P3), Université de Toulouse, CNRS, UPS, 31062 Toulouse Cedex 9, France
⁴ Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona, Spain
⁵ Department of Physics and Astronomy, Ghent University, Proeftuinstraat 86, 9000 Ghent, Belgium
⁶ SUPA, University of Glasgow, Glasgow, G12 8QQ, UK
⁷ Department of Physics & Astronomy, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
⁸ Université Paris Cité, CNRS, Astroparticule et Cosmologie, 75013 Paris, France

Received: 25 May 2023
Accepted: 1 December 2023

Abstract

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.