Browsing by Author "Irfan, Melis O"

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    Cleaning foregrounds from single-dish 21 cm intensity maps with Kernel principal component analysis
    (Oxford University Press, 2021) Irfan, Melis O; Bull, Phillip
    he 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. © 2021 The Author(s).
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    Mitigating the effect of 1/f noise on the detection of the H I intensity mapping power spectrum from single-dish measurements
    (Oxford University Press, 2024) Irfan, Melis O; Santos, Mario G; Bull, Philip; Wang, Jingying
    We present and compare several methods to mitigate time-correlated (1/f) noise within the H I intensity mapping component of the MeerKAT Large Area Synoptic Survey (MeerKLASS). By simulating scan strategies, the H I signal, foreground emissions, white and correlated noise, we assess the ability of various data-processing pipelines to recover the power spectrum of H I brightness temperature fluctuations. We use MeerKAT pilot data to assess the level of 1/f noise expected for the MeerKLASS survey and use these measurements to create realistic levels of time-correlated noise for our simulations. We find the time-correlated noise component within the pilot data to be between 10 and 20 times higher than the white noise level at the scale of k = 0.04 Mpc−1. Having determined that the MeerKAT 1/f noise is partially correlated across all the frequency channels, we employ Singular Value Decomposition (SVD) as a technique to remove both the 1/f noise and Galactic foregrounds but find that over-cleaning results in the removal of H I power at large (angular and radial) scales; a power loss of 40 per cent is seen for a 3-mode SVD clean at the scale of k = 0.04 Mpc−1. We compare the impact of map-making using weighting by the full noise covariance (i.e. including a 1/f component), as opposed to just a simple unweighted binning, finding that including the time-correlated noise information reduces the excess power added by 1/f noise by up to 30 per cent.
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    Simulating a full-sky high resolution Galactic synchrotron spectral index map using neural networks
    (Monthly Notices of the Royal Astronomical Society, 2023) Irfan, Melis O
    We present a model for the full-sky diffuse Galactic synchrotron spectral index with an appropriate level of spatial structure for a resolution of 56 arcmin (to match the resolution of the Haslam 408 MHz data). Observational data at 408 MHz and 23 GHz have been used to provide spectral indices at a resolution of 5 degrees. In this work, we make use of convolutional neural networks to provide a realistic proxy for the higher resolution information, in place of the genuine structure. Our deep learning algorithm has been trained using 14.4 arcmin observational data from the 1.4 GHz Parkes radio continuum survey. We compare synchrotron emission maps constructed by extrapolating the Haslam data using various spectral index maps, of different angular resolution, with the Global Sky Model.