Abstract
The thermal Sunyaev-Zeldovich (SZ) effect and the X-ray emission offer separate and highly complementary probes of the thermodynamics of the intracluster medium, particularly on their radial dependence. We already released JoXSZ, the first publicly available code designed to jointly fit SZ and X-ray data coming from various instruments to derive the thermodynamic radial profiles of galaxy clusters, including mass. JoXSZ follows a fully Bayesian forward-modelling approach, adopts flexible parametrization for the thermodynamic profiles and includes many useful options that users can customize according to their needs. We are including shear measurement in our joint analysis, and moving from single-cluster to multi-cluster analyses, allowing to quantify the heterogeneity of thermodynamic properties within the cluster population. At the same time, we are creating a suitable framework that effciently stores and optimally processes huge volumes of data being released by the current and new generation surveys.
Highlights
Galaxy clusters are the largest and most massive gravitationally bound objects in the Universe, and they o↵er a unique tracer of cosmic evolution [1]
The thermodynamic properties of a galaxy cluster can be probed through observations in the optical, X-ray, or microwave bands via the Sunyaev-Zeldovich (SZ) e↵ect [2, 3], that we are combining into a single joint analysis
[4, source code available on GitHub1], the first publicly available code designed to jointly fit SZ and X-ray data to derive the thermodynamic profiles of galaxy clusters
Summary
Galaxy clusters are the largest and most massive gravitationally bound objects in the Universe, and they o↵er a unique tracer of cosmic evolution [1]. We are currently working to include shear data as a third complementary component in the fit and to move from single-cluster to multi-cluster analyses, allowing to gather thermodynamic measurements on populations of clusters Since it will involve large amount of observations coming from current and generation facilities, we are creating a suitable framework that efficiently stores and optimally processes huge volumes of data. JoXSZ accounts for beam smearing and data analysis transfer function, for the SZ calibration uncertainty and X-ray and SZ background level systematics It adopts extremely flexible parametrization for the thermodynamic profiles and it employs a consistent temperature across the various parts of the code, allowing for di↵erences between X-ray and SZ gas mass weighted temperatures when required by the user, and calculates the correct Poisson-Gauss expression for the joint likelihood. JoXSZ merges and extends these two processes into a unique joint and consistent model based on a Markov chain Monte Carlo (MCMC) fitting algorithm
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