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arXiv · 2311.02866

Quantifying the tension between cosmological models and JWST red candidate massive galaxies

Junchao Wang·Zhiqi Huang·Lu Huang·Jianqi Liu
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Abstract

We develop a Python tool to estimate the tail distribution of the number of dark matter halos beyond a mass threshold and in a given volume in a light-cone. The code is based on the extended Press-Schechter model and is computationally efficient, typically taking a few seconds on a personal laptop for a given set of cosmological parameters. The high efficiency of the code allows a quick estimation of the tension between cosmological models and the red candidate massive galaxies released by the James Webb Space Telescope, as well as scanning the theory space with the Markov Chain Monte Carlo method. As an example application, we use the tool to study the cosmological implication of the candidate galaxies presented in Labb\'e et al. (2023). The standard $\Lambda$ cold dark matter ($\Lambda$CDM) model is well consistent with the data if the star formation efficiency can reach $\sim 0.3$ at high redshift. For a low star formation efficiency $\epsilon \sim 0.1$, $\Lambda$CDM model is disfavored at $\sim 2\sigma$-$3\sigma$ confidence level.

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Junchao Wang, Zhiqi Huang, Lu Huang, Jianqi Liu. 2023-11-06. Quantifying the tension between cosmological models and JWST red candidate massive galaxies. https://doi.org/10.1088/1674-4527%2Fad2cd3

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