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      Impact of massive binary star and cosmic evolution on gravitational wave observations I: black hole–neutron star mergers

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          ABSTRACT

          Mergers of black hole–neutron star (BHNS) binaries have now been observed by gravitational wave (GW) detectors with the recent announcement of GW200105 and GW200115. Such observations not only provide confirmation that these systems exist but will also give unique insights into the death of massive stars, the evolution of binary systems and their possible association with gamma-ray bursts, r-process enrichment, and kilonovae. Here, we perform binary population synthesis of isolated BHNS systems in order to present their merger rate and characteristics for ground-based GW observatories. We present the results for 420 different model permutations that explore key uncertainties in our assumptions about massive binary star evolution (e.g. mass transfer, common-envelope evolution, supernovae), and the metallicity-specific star formation rate density, and characterize their relative impacts on our predictions. We find intrinsic local BHNS merger rates spanning $\mathcal {R}_{\rm {m}}^0 \approx\(4–830 \)\, \rm {Gpc}^{-3}$$\, \rm {yr}^{-1}$ for our full range of assumptions. This encompasses the rate inferred from recent BHNS GW detections and would yield detection rates of $\mathcal {R}_{\rm {det}} \approx 1$–180$\, \rm {yr}^{-1}\(for a GW network consisting of LIGO, Virgo, and KAGRA at design sensitivity. We find that the binary evolution and metallicity-specific star formation rate density each impacts the predicted merger rates by order \)\mathcal {O}(10)$. We also present predictions for the GW-detected BHNS merger properties and find that all 420 model variations predict that $\lesssim 5{{\ \rm per\ cent}}\(of the BHNS mergers have BH masses \)m_{\rm {BH}} \gtrsim 18\, \rm {M}_{\odot }$, total masses $m_{\rm {tot}} \gtrsim 20\, \rm {M}_{\odot }$, chirp masses ${\mathcal {M}}_{\rm {c}} \gtrsim 5.5\, \rm {M}_{\odot }$, and mass ratios qf ≳ 12 or qf ≲ 2. Moreover, we find that massive NSs with $m_{\rm {NS}} \gt 2\, \rm {M}_{\odot }\(are expected to be commonly detected in BHNS mergers in almost all our model variations. Finally, a wide range of \)\sim 0{{\ \rm per\ cent}}\(to \)70{{\ \rm per\ cent}}$ of the BHNS mergers are predicted to eject mass during the merger. Our results highlight the importance of considering variations in binary evolution and cosmological models when predicting, and eventually evaluating, populations of BHNS mergers.

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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

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                Journal
                Monthly Notices of the Royal Astronomical Society
                Oxford University Press (OUP)
                0035-8711
                1365-2966
                December 2021
                October 27 2021
                December 2021
                October 27 2021
                September 23 2021
                : 508
                : 4
                : 5028-5063
                Affiliations
                [1 ]Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA
                [2 ]Monash Centre for Astrophysics, School of Physics and Astronomy, Monash University, Clayton, Victoria 3800, Australia
                [3 ]The ARC Center of Excellence for Gravitational Wave Discovery, OzGrav, Hawthorn VIC 3122, Australia
                [4 ]Birmingham Institute for Gravitational Wave Astronomy and School of Physics and Astronomy, University of Birmingham, Birmingham B15 2TT, UK
                [5 ]DARK, Niels Bohr Institute, University of Copenhagen, Jagtvej 128, DK-2200, Copenhagen, Denmark
                [6 ]Center for Astrophysics and Supercomputing, Swinburne University of Technology, Hawthorn VIC 3122, Australia
                [7 ]Institute of Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen, PO Box 9010, NL-6500 GL Nijmegen, the Netherlands
                [8 ]School of Astronomy & Space Science, University of the Chinese Academy of Sciences, Beijing 100012, China
                [9 ]National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China
                [10 ]Anton Pannekoek Institute for Astronomy and GRAPPA, University of Amsterdam, Postbus 94249, NL-1090 GE Amsterdam, the Netherlands
                [11 ]Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Straße 1, D-85741 Garching, Germany
                Article
                10.1093/mnras/stab2716
                0ff7742d-6744-4c22-bdc3-ac4fd6e3c495
                © 2021

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

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