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      Cleaning foregrounds from single-dish 21 cm intensity maps with Kernel principal component analysis

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          ABSTRACT

          The high dynamic range between contaminating foreground emission and the fluctuating 21 cm brightness temperature field is one of the most problematic characteristics of 21 cm intensity mapping data. While these components would ordinarily have distinctive frequency spectra, making it relatively easy to separate them, instrumental effects and calibration errors further complicate matters by modulating and mixing them together. A popular class of foreground cleaning method are unsupervised techniques related to principal component analysis (PCA), which exploit the different shapes and amplitudes of each component’s contribution to the covariance of the data in order to segregate the signals. These methods have been shown to be effective at removing foregrounds, while also unavoidably filtering out some of the 21 cm signal too. In this paper we examine, for the first time in the context of 21 cm intensity mapping, a generalized method called Kernel PCA, which instead operates on the covariance of non-linear transformations of the data. This allows more flexible functional bases to be constructed, in principle allowing a cleaner separation between foregrounds and the 21 cm signal to be found. We show that Kernel PCA is effective when applied to simulated single-dish (auto-correlation) 21 cm data under a variety of assumptions about foregrounds models, instrumental effects etc. It presents a different set of behaviours to PCA, e.g. in terms of sensitivity to the data resolution and smoothing scale, outperforming it on intermediate to large scales in most scenarios.

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            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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              The NumPy Array: A Structure for Efficient Numerical Computation

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                Author and article information

                Journal
                Monthly Notices of the Royal Astronomical Society
                Oxford University Press (OUP)
                0035-8711
                1365-2966
                December 2021
                October 20 2021
                December 2021
                October 20 2021
                October 05 2021
                : 508
                : 3
                : 3551-3568
                Affiliations
                [1 ]Department of Physics and Astronomy, University of Western Cape, Cape Town 7535, South Africa
                [2 ]Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK
                Article
                10.1093/mnras/stab2855
                2d394d92-3a47-4b7c-b7fa-086e5b860bae
                © 2021

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


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