cosmocnc: A fast, flexible, and accurate framework for galaxy cluster number count likelihood computation

Abstract We introduce cosmocnc , a new framework for computing the number count likelihood of galaxy cluster catalogues in a fast, flexible and accurate way. cosmocnc offers three types of likelihoods: an unbinned, a binned, and an extreme value likelihood. It also supports the addition of stacked cluster data, which is modelled consistently with the cluster catalogue. The unbinned likelihood, which is the main focus of the framework, can take an arbitrary number of mass observables as input and deal with several complexities in the data, such as variations in the properties of the cluster observable across the survey footprint, the possibility of different clusters having measurements for different combinations of mass observables, redshift measurement uncertainties, and the presence on unconfirmed detections in the catalogue. If there are more than one mass observables, the unbinned likelihood is computed with a novel approach, the backward convolutional approach. After introducing the framework in detail, we demonstrate its application with synthetic Simons-Observatory-like catalogues, finding excellent agreement between their properties and cosmocnc 's predictions and obtaining constraints on cosmological and scaling relation parameters featuring negligible biases. A Python implementation of the cosmocnc framework is publicly available at https://github.com/inigozubeldia/cosmocnc .