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      Constraining the Cosmic Baryon Distribution with Fast Radio Burst Foreground Mapping

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          Abstract

          The dispersion measure of fast radio bursts (FRBs) encodes the integrated electron density along the line of sight, which is typically dominated by the intergalactic medium contribution in the case of extragalactic FRBs. In this paper, we show that incorporating wide-field spectroscopic galaxy survey data in the foreground of localized FRBs can significantly improve constraints on the partition of diffuse cosmic baryons. Using mock dispersion measures and realistic light-cone galaxy catalogs derived from the Millennium simulation, we define spectroscopic surveys that can be carried out with 4 and 8 m class wide-field spectroscopic facilities. On these simulated surveys, we carry out Bayesian density reconstructions in order to estimate the foreground matter density field. In comparison with the “true” matter density field, we show that these can help reduce the uncertainties in the foreground structures by ∼2–3× compared to cosmic variance. We calculate the Fisher matrix to forecast that N = 30 (96) localized FRBs should be able to constrain the diffuse cosmic baryon fraction to ∼10% (∼5%) and parameters governing the size and baryon fraction of galaxy circumgalactic halos to within ∼20%–25% (∼8%–12%). From the Fisher analysis, we show that the foreground data increase the sensitivity of localized FRBs toward our parameters of interest by ∼25×. We briefly introduce FLIMFLAM, an ongoing galaxy redshift survey that aims to obtain foreground data on ∼30 localized FRB fields.

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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.

                Author and article information

                Contributors
                Journal
                The Astrophysical Journal
                ApJ
                American Astronomical Society
                0004-637X
                1538-4357
                March 22 2022
                March 01 2022
                March 22 2022
                March 01 2022
                : 928
                : 1
                : 9
                Article
                10.3847/1538-4357/ac4f62
                30c572a0-0eb3-4ca5-850e-3181304ddf7a
                © 2022

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

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