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      King Ghidorah Supercluster: Mapping the light and dark matter in a new supercluster at z = 0.55 using the subaru hyper suprime-cam

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          ABSTRACT

          This paper reports our discovery of the most massive supercluster, termed the King Ghidorah Supercluster (KGSc), at z = 0.50–0.64 in the Third Public Data Release of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP PDR3) over 690 deg2, as well as an initial result for a galaxy and dark matter mapping. The primary structure of the KGSc comprises triple broad weak-lensing (WL) peaks over 70 comoving Mpc. Such extensive WL detection at z > 0.5 can only currently be achieved using the wide-field high-quality images produced by the HSC-SSP. The structure is also contiguous with multiple large-scale structures across a ∼400 comoving Mpc scale. The entire field has a notable overdensity (δ = 14.7 ± 4.5) of red-sequence clusters. Additionally, large-scale underdensities can be found in the foreground along the line of sight. We confirmed the overdensities in stellar mass and dark matter distributions to be tightly coupled and estimated the total mass of the main structure to be 1 × 1016 solar masses, according to the mock data analyses based on large-volume cosmological simulations. Further, upcoming wide-field multi-object spectrographs such as the Subaru Prime Focus Spectrograph may aid in providing additional insights into distant superclusters beyond the 100 Mpc scale.

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          Matplotlib: A 2D Graphics Environment

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            Is Open Access

            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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              Astropy: A community Python package for astronomy

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                Author and article information

                Contributors
                (View ORCID Profile)
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                Journal
                Monthly Notices of the Royal Astronomical Society: Letters
                Oxford University Press (OUP)
                1745-3925
                1745-3933
                February 2023
                November 24 2022
                February 2023
                November 24 2022
                November 26 2022
                : 519
                : 1
                : L45-L50
                Article
                10.1093/mnrasl/slac150
                90ae9b5a-190d-45bb-8ee9-d574a1f2b125
                © 2022

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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