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.

中文翻译：

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cosmocnc：一个快速、灵活、准确的星系团数量计算似然计算框架


我们介绍了 cosmocnc，这是一个以快速、灵活和准确的方式计算星系团星表数量计数可能性的新框架。cosmocnc 提供三种类型的可能性：非 binned、bin 和极值可能性。它还支持添加堆叠集群数据，这些数据的建模与集群目录一致。非装箱似然是框架的主要关注点，它可以将任意数量的质量可观察对象作为输入，并处理数据中的多种复杂性，例如在整个调查足迹中可观察的集群属性的变化，不同集群对不同质量可观察对象组合进行测量的可能性， 红移测量的不确定性，以及目录中未确认的检测结果。如果有多个质量可观察对象，则使用一种新方法（向后卷积方法）计算未分箱的可能性。在详细介绍了该框架之后，我们用类似 Simons-Observatory 的合成星表演示了它的应用，发现它们的性质与 cosmocnc 的预测之间有很好的一致性，并获得了对宇宙学和缩放关系参数的约束，这些参数的偏差可以忽略不计。cosmocnc 框架的 Python 实现在 https://github.com/inigozubeldia/cosmocnc 上公开提供。