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      On the gamma-ray emission from the core of the Sagittarius dwarf galaxy

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          ABSTRACT

          We use Fermi-LAT data to analyse the faint gamma-ray source located at the centre of the Sagittarius (Sgr) dwarf spheroidal galaxy. In the 4FGL-DR3 catalogue, this source is associated with the globular cluster, M54. We investigate the spectral energy distribution and spatial extension of this source, with the goal of testing two hypotheses: (1) the emission is due to millisecond pulsars within M54, or (2) the emission is due to annihilating dark matter from the Sgr halo. For the pulsar interpretation, we consider a two-component model which describes both the lower-energy magnetospheric emission and possible high-energy emission arising from inverse Compton scattering. We find that this source has a point-like morphology at low energies, consistent with magnetospheric emission, and find no evidence for a higher-energy component. For the dark matter interpretation, we find the signal favours a dark matter mass of mχ = 29.6 ± 5.8 GeV and an annihilation cross section of $\sigma v = (2.1 \pm 0.59) \times 10^{-26} \, \text{cm}^3\(s−1 for the \)b \bar{b}\(channel (or mχ = 8.3 ± 3.8 GeV and \)\sigma v = (0.90 \pm 0.25) \times 10^{-26} \, \text{cm}^3\(s−1 for the τ+τ− channel), when adopting a J-factor of \)J=10^{19.6} \, \text{GeV}^2 \, \text{cm}^{-5}$. This parameter space is consistent with gamma-ray constraints from other dwarf galaxies and with dark matter interpretations of the Galactic Centre Gamma-Ray Excess.

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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
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                Journal
                Monthly Notices of the Royal Astronomical Society
                Oxford University Press (OUP)
                0035-8711
                1365-2966
                September 2023
                July 24 2023
                September 2023
                July 24 2023
                July 13 2023
                : 524
                : 3
                : 4574-4585
                Article
                10.1093/mnras/stad2074
                c9dc3ee3-6bc1-48ae-8bee-6b1546de5265
                © 2023

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

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