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      Survival in the Neptune desert: LTT 9779 b kept its atmosphere thanks to an unusually X-ray faint host star

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          ABSTRACT

          The Neptunian desert is a region in period-radius parameter space with very few Neptune-sized planets at short orbital periods. Amongst these, LTT 9779 b is the only known Neptune with a period shorter than 1 d to retain a significant H–He atmosphere. If the Neptune desert is the result of X-ray/EUV-driven photoevaporation, it is surprising that the atmosphere of LTT 9779 b survived the intense bombardment of high-energy photons from its young host star. However, the star has low measured rotational broadening, which points to the possibility of an anomalously slow spin period and hence a faint X-ray emission history that may have failed to evaporate the planet’s atmosphere. We observed LTT 9779 with XMM-Newton and measured an upper limit for its X-ray luminosity that is a factor of 15 lower than expected for its age. We also simulated the evaporation past of LTT 9779 b and found that the survival of its atmosphere to the present day is consistent with an unusually faint XUV irradiation history that matches both the X-ray and rotation velocity measurements. We conclude that the anomalously low X-ray irradiation of the one Neptune seen to survive in Neptunian desert supports the interpretation of the desert as primarily a result of photoevaporation.

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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
                January 2024
                October 28 2023
                January 2024
                October 28 2023
                October 25 2023
                : 527
                : 1
                : 911-918
                Article
                10.1093/mnras/stad3263
                57438751-037a-4af5-a96c-cbf12e2ca1c2
                © 2023

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

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