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      QUIJOTE scientific results – IX. Radio sources in the QUIJOTE-MFI wide survey maps

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          ABSTRACT

          We present the catalogue of Q-U-I JOint TEnerife (QUIJOTE) Wide Survey radio sources extracted from the maps of the Multi-Frequency Instrument compiled between 2012 and 2018. The catalogue contains 786 sources observed in intensity and polarization, and is divided into two separate sub-catalogues: one containing 47 bright sources previously studied by the Planck collaboration and an extended catalogue of 739 sources either selected from the Planck Second Catalogue of Compact Sources or found through a blind search carried out with a Mexican Hat 2 wavelet. A significant fraction of the sources in our catalogue (38.7 per cent) are within the |b| ≤ 20° region of the Galactic plane. We determine statistical properties for those sources that are likely to be extragalactic. We find that these statistical properties are compatible with currently available models, with a ∼1.8 Jy completeness limit at 11 GHz. We provide the polarimetric properties of (38, 33, 31, 23) sources with P detected above the $99.99{{\ \rm per\, cent}}$ significance level at (11, 13, 17, 19) GHz respectively. Median polarization fractions are in the 2.8–4.7 per cent range in the 11–19 GHz frequency interval. We do not distinguish between Galactic and extragalactic sources here. The results presented here are consistent with those reported in the literature for flat- and steep-spectrum radio sources.

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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
                Monthly Notices of the Royal Astronomical Society
                Oxford University Press (OUP)
                0035-8711
                1365-2966
                March 2023
                January 09 2023
                March 2023
                January 09 2023
                January 12 2023
                : 519
                : 3
                : 3526-3545
                Article
                10.1093/mnras/stac3657
                9fc1b92a-0913-4ccb-91d2-8b4fffcf6692
                © 2023

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

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