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      Testing the Limits of AGN Feedback and the Onset of Thermal Instability in the Most Rapidly Star-forming Brightest Cluster Galaxies

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          Abstract

          We present new, deep, narrow- and broadband Hubble Space Telescope observations of seven of the most star-forming brightest cluster galaxies (BCGs). Continuum-subtracted [O II] maps reveal the detailed, complex structure of warm ( T ∼ 10 4 K) ionized gas filaments in these BCGs, allowing us to measure spatially resolved star formation rates (SFRs) of ∼60–600 M yr −1. We compare the SFRs in these systems and others from the literature to their intracluster medium cooling rates ( M ̇ cool ), measured from archival Chandra X-ray data, finding a best-fit relation of log ( SFR ) = ( 1.66 ± 0.17 ) log ( M ̇ cool ) + (−3.22 ± 0.38) with an intrinsic scatter of 0.39 ± 0.09 dex. This steeper-than-unity slope implies an increasingly efficient conversion of hot ( T ∼ 10 7 K) gas into young stars with increasing M ̇ cool , or conversely a gradual decrease in the effectiveness of AGN feedback in the strongest cool cores. We also seek to understand the physical extent of these multiphase filaments that we observe in cluster cores. We show, for the first time, that the average extent of the multiphase gas is always smaller than the radii at which the cooling time reaches 1 Gyr, the t cool/ t ff profile flattens, and that X-ray cavities are observed. This implies a close connection between the multiphase filaments, the thermodynamics of the cooling core, and the dynamics of X-ray bubbles. Interestingly, we find a one-to-one correlation between the average extent of cool multiphase filaments and the radius at which the cooling time reaches 0.5 Gyr, which may be indicative of a universal condensation timescale in cluster cores.

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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
                November 29 2022
                December 01 2022
                November 29 2022
                December 01 2022
                : 940
                : 2
                : 140
                Article
                10.3847/1538-4357/ac9790
                7310ed70-cff4-43bf-a9bc-7442bdafd7dc
                © 2022

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

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