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      Diving Beneath the Sea of Stellar Activity: Chromatic Radial Velocities of the Young AU Mic Planetary System

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      The Astronomical Journal
      American Astronomical Society

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          Abstract

          We present updated radial-velocity (RV) analyses of the AU Mic system. AU Mic is a young (22 Myr) early-M dwarf known to host two transiting planets— P b ∼ 8.46 days, R b = 4.38 0.18 + 0.18 R , P c ∼ 18.86 days, R c = 3.51 0.16 + 0.16 R . With visible RVs from Calar Alto high-Resolution search for M dwarfs with Exo-earths with Near-infrared and optical echelle Spectrographs (CARMENES)-VIS, CHIRON, HARPS, HIRES, M inerva-Australis, and Tillinghast Reflector Echelle Spectrograph, as well as near-infrared (NIR) RVs from CARMENES-NIR, CSHELL, IRD, iSHELL, NIRSPEC, and SPIRou, we provide a 5 σ upper limit to the mass of AU Mic c of M c ≤ 20.13 M and present a refined mass of AU Mic b of M b = 20.12 1.57 + 1.72 M . Used in our analyses is a new RV modeling toolkit to exploit the wavelength dependence of stellar activity present in our RVs via wavelength-dependent Gaussian processes. By obtaining near-simultaneous visible and near-infrared RVs, we also compute the temporal evolution of RV “color” and introduce a regressional method to aid in isolating Keplerian from stellar activity signals when modeling RVs in future works. Using a multiwavelength Gaussian process model, we demonstrate the ability to recover injected planets at 5 σ significance with semi-amplitudes down to ≈10 m s −1 with a known ephemeris, more than an order of magnitude below the stellar activity amplitude. However, we find that the accuracy of the recovered semi-amplitudes is ∼50% for such signals with our model.

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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 Astronomical Journal
                AJ
                American Astronomical Society
                0004-6256
                1538-3881
                December 07 2021
                December 01 2021
                December 07 2021
                December 01 2021
                : 162
                : 6
                : 295
                Article
                10.3847/1538-3881/ac2c80
                c9ebb967-d5b2-4c5a-a590-2fc64ce06e6b
                © 2021

                https://iopscience.iop.org/page/copyright

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