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      Stress testing ΛCDM with high-redshift galaxy candidates

      research-article
      Nature Astronomy
      Nature Publishing Group UK
      Cosmology, Galaxies and clusters

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          Abstract

          Early data from the James Webb Space Telescope (JWST) have revealed a bevy of high-redshift galaxy candidates with unexpectedly high stellar masses. An immediate concern is the consistency of these candidates with galaxy formation in the standard ΛCDM cosmological model, wherein the stellar mass ( M ) of a galaxy is limited by the available baryonic reservoir of its host dark matter halo. The mass function of dark matter haloes therefore imposes an absolute upper limit on the number density n (> M ,  z) and stellar mass density ρ (> M ,  z) of galaxies more massive than M at any epoch z. Here I show that the most massive galaxy candidates in JWST observations at z ≈ 7–10 lie at the very edge of these limits, indicating an important unresolved issue with the properties of galaxies derived from the observations, how galaxies form at early times in ΛCDM or within this standard cosmology itself.

          Abstract

          Early James Webb Space Telescope (JWST) results suggest a high level of star formation in the first 500 million years of the Universe. A study of the available mass from dark matter haloes shows that unexpectedly high-mass JWST galaxy candidates may challenge the prevailing cosmological model.

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          SciPy 1.0: fundamental algorithms for scientific computing in Python

          SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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            Matplotlib: A 2D Graphics Environment

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              Array programming with NumPy

              Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
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                Author and article information

                Contributors
                mbk@astro.as.utexas.edu
                Journal
                Nat Astron
                Nat Astron
                Nature Astronomy
                Nature Publishing Group UK (London )
                2397-3366
                13 April 2023
                13 April 2023
                2023
                : 7
                : 6
                : 731-735
                Affiliations
                GRID grid.89336.37, ISNI 0000 0004 1936 9924, Department of Astronomy, , The University of Texas at Austin, ; Austin, TX USA
                Author information
                http://orcid.org/0000-0002-9604-343X
                Article
                1937
                10.1038/s41550-023-01937-7
                10281863
                7691ab2e-8922-4a29-ae03-f3860470b9ca
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 4 January 2023
                : 7 March 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000001, National Science Foundation (NSF);
                Award ID: AST-1910346
                Award ID: AST-2108962
                Award Recipient :
                Funded by: FundRef https://doi.org/100000104, National Aeronautics and Space Administration (NASA);
                Award ID: 80NSSC22K0827
                Award ID: HST-GO-15658
                Award ID: HST-GO-15901
                Award ID: HST-GO-15902
                Award ID: HST-AR-16159
                Award ID: HST-GO-16226
                Award Recipient :
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                © Springer Nature Limited 2023

                cosmology,galaxies and clusters
                cosmology, galaxies and clusters

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