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      ALMA-IMF : II. Investigating the origin of stellar masses: Continuum images and data processing

      , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , The ALMA-IMF Consortium
      Astronomy & Astrophysics
      EDP Sciences

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          Abstract

          We present the first data release of the ALMA-IMF Large Program, which covers the 12m-array continuum calibration and imaging. The ALMA-IMF Large Program is a survey of fifteen dense molecular cloud regions spanning a range of evolutionary stages that aims to measure the core mass function. We describe the data acquisition and calibration done by the Atacama Large Millimeter/submillimeter Array (ALMA) observatory and the subsequent calibration and imaging we performed. The image products are combinations of multiple 12 m array configurations created from a selection of the observed bandwidth using multi-term, multi-frequency synthesis imaging and deconvolution. The data products are self-calibrated and exhibit substantial noise improvements over the images produced from the delivered data. We compare different choices of continuum selection, calibration parameters, and image weighting parameters, demonstrating the utility and necessity of our additional processing work. Two variants of continuum selection are used and will be distributed: the “best-sensitivity” ( bsens) data, which include the full bandwidth, including bright emission lines that contaminate the continuum, and “cleanest” ( cleanest), which select portions of the spectrum that are unaffected by line emission. We present a preliminary analysis of the spectral indices of the continuum data, showing that the ALMA products are able to clearly distinguish free-free emission from dust emission, and that in some cases we are able to identify optically thick emission sources. The data products are made public with this release.

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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
                Journal
                Astronomy & Astrophysics
                A&A
                EDP Sciences
                0004-6361
                1432-0746
                June 2022
                May 31 2022
                June 2022
                : 662
                : A9
                Article
                10.1051/0004-6361/202141681
                9d3630aa-d5ed-49e7-85ad-38417233057a
                © 2022

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

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