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      EIGER. I. A Large Sample of [O iii]-emitting Galaxies at 5.3 < z < 6.9 and Direct Evidence for Local Reionization by Galaxies

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          Abstract

          We present a first sample of 117 [O iii] λλ4960, 5008–selected star-forming galaxies at 5.33 < z < 6.93 detected in JWST/NIRCam 3.5 μm slitless spectroscopy of a 6 .′ 5 × 3 .′ 4 field centered on the hyperluminous quasar SDSS J0100+2802, obtained as part of the Emission-line galaxies and Intergalactic Gas in the Epoch of Reionization (EIGER) survey. Three prominent galaxy overdensities are observed, one of them at the redshift of the quasar. Galaxies are found within 200 pkpc and 105 km s −1 of four known metal absorption-line systems. We focus on the role of the galaxies in ionizing the intergalactic medium (IGM) during the later stages of cosmic reionization and construct the mean Ly α and Ly β transmission as a function of distance from the galaxies. At the lowest redshifts in our study, 5.3 < z < 5.7, the IGM transmission rises monotonically with distance from the galaxies, as seen previously at lower redshifts. In contrast, at 5.7 < z < 6.14, the transmission of both Ly α and Ly β first increases with distance but then peaks at a distance of 5 cMpc before declining. Finally, in the region 6.15 < z < 6.26, where the additional ionizing radiation from the quasar dominates, the monotonic increase in transmission with distance is reestablished. This result is interpreted to represent evidence that the transmission of the IGM at z ∼ 5.9 toward J0100+2802 results from the “local” ionizing radiation of galaxies that dominates over the much-reduced cosmic background.

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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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                Journal
                The Astrophysical Journal
                ApJ
                American Astronomical Society
                0004-637X
                1538-4357
                June 12 2023
                June 01 2023
                June 12 2023
                June 01 2023
                : 950
                : 1
                : 66
                Article
                10.3847/1538-4357/acc588
                6c89f708-8f33-47f3-b956-cb91e4a2cf41
                © 2023

                http://creativecommons.org/licenses/by/4.0/

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