arXiv subjects

Melis O. Irfan

Publications and source records attributed to Melis O. Irfan.

23 records · Page 2Linked to original sources

arXiv2021

Cleaning foregrounds from single-dish 21cm intensity maps with Kernel Principal Component Analysis

The high dynamic range between contaminating foreground emission and the fluctuating 21cm brightness temperature field is one of the most problematic characteristics of 21cm 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 21cm signal too. In this paper we examine, for the first time in the context of 21cm intensity mapping, a generalised 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 21cm signal to be found. We show that Kernel PCA is effective when applied to simulated single-dish (autocorrelation) 21cm 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.

astro-ph.CO↗

arXiv2021

HI intensity mapping with MeerKAT: Calibration pipeline for multi-dish autocorrelation observations

While most purpose-built 21cm intensity mapping experiments are close-packed interferometer arrays, general-purpose dish arrays should also be capable of measuring the cosmological 21cm signal. This can be achieved most efficiently if the array is used as a collection of scanning autocorrelation dishes rather than as an interferometer. As a first step towards demonstrating the feasibility of this observing strategy, we show that we are able to successfully calibrate dual-polarisation autocorrelation data from 64 MeerKAT dishes in the L-band (856-1712 MHz, 4096 channels), with 10.5 hours of data retained from six nights of observing. We describe our calibration pipeline, which is based on multi-level RFI flagging, periodic noise diode injection to stabilise gain drifts and an absolute calibration based on a multi-component sky model. We show that it is sufficiently accurate to recover maps of diffuse celestial emission and point sources over a 10 deg x 30 deg patch of the sky overlapping with the WiggleZ 11hr field. The reconstructed maps have a good level of consistency between per-dish maps and external datasets, with the estimated thermal noise limited to 1.4 x the theoretical noise level (~ 2 mK). The residual maps have rms amplitudes below 0.1 K, corresponding to <1% of the model temperature. The reconstructed Galactic HI intensity map shows excellent agreement with the Effelsberg-Bonn HI Survey, and the flux of the radio galaxy 4C+03.18 is recovered to within 3.6%, which demonstrates that the autocorrelation can be successfully calibrated to give the zero-spacing flux and potentially help in the imaging of MeerKAT interferometric data. Our results provide a positive indication towards the feasibility of using MeerKAT and the future SKA to measure the HI intensity mapping signal and probe cosmology on degree scales and above.

astro-ph.CO↗

arXiv2021

21cm foregrounds and polarization leakage: a user's guide on cleaning and mitigation strategies

The success of HI intensity mapping is largely dependent on how well 21cm foreground contamination can be controlled. In order to progress our understanding further, we present a range of simulated foreground data from four different $\sim3000$\,deg$^2$ sky regions, with and without effects from polarization leakage. Combining these with underlying cosmological HI simulations creates a range of single-dish intensity mapping test cases that require different foreground treatments. This allows us to conduct the most generalized study to date into 21cm foregrounds and their cleaning techniques for the post-reionization era. We first provide a pedagogical review of the most commonly used blind foreground removal techniques (PCA/SVD, FASTICA, GMCA). We also trial a non-blind parametric fitting technique and discuss potential hybridization of methods. We highlight the similarities and differences in these techniques finding that the blind methods produce near equivalent results, and we explain the fundamental reasons for this. The simulations allow an exact decomposition of the resulting cleaned data and we analyse the contribution from foreground residuals. Our results demonstrate that polarized foreground residuals should be generally subdominant to HI on small scales ($k\gtrsim0.1\,h\,\text{Mpc}^{-1}$). However, on larger scales, results are more region dependent. In some cases, aggressive cleans severely damp HI power but still leave dominant foreground residuals. We also demonstrate the gain from cross-correlations with optical galaxy surveys, where extreme levels of residual foregrounds can be circumvented. However, these residuals still contribute to errors and we discuss the optimal balance between over- and under-cleaning.

astro-ph.CO↗

arXiv2020

Recovery of 21 cm intensity maps with sparse component separation

21 cm intensity mapping has emerged as a promising technique to map the large-scale structure of the Universe. However, the presence of foregrounds with amplitudes orders of magnitude larger than the cosmological signal constitutes a critical challenge. Here, we test the sparsity-based algorithm Generalised Morphological Component Analysis (GMCA) as a blind component separation technique for this class of experiments. We test the GMCA performance against realistic full-sky mock temperature maps that include, besides astrophysical foregrounds, also a fraction of the polarized part of the signal leaked into the unpolarized one, a very troublesome foreground to subtract, usually referred to as polarization leakage. To our knowledge, this is the first time the removal of such component is performed with no prior assumption. We assess the success of the cleaning by comparing the true and recovered power spectra, in the angular and radial directions. In the best scenario looked at, GMCA is able to recover the input angular (radial) power spectrum with an average bias of $\sim 5\%$ for $\ell>25$ ($20 - 30 \%$ for $k_{\parallel} \gtrsim 0.02 \,h^{-1}$Mpc), in the presence of polarization leakage. Our results are robust also when up to $40\%$ of channels are missing, mimicking a Radio Frequency Interference (RFI) flagging of the data. Having quantified the notable effect of polarisation leakage on our results, in perspective we advocate the use of more realistic simulations when testing 21 cm intensity mapping capabilities.

astro-ph.CO↗

arXiv2019

Determining thermal dust emission from Planck HFI data using a sparse, parametric technique

Context: The Planck data releases have provided the community with sub-millimetre and radio observations of the full-sky at unprecedented resolutions. We make use of the Planck 353, 545 and 857 GHz maps alongside the IRAS 3000 GHz map. These maps contain information on the cosmic microwave background (CMB), cosmic infrared background (CIB), extragalactic point sources and diffuse thermal dust emission. Aims: We aim to determine the modified black body (MBB) model parameters of thermal dust emission in total intensity and produce all sky maps of pure thermal dust, having separated this Galactic component from the CMB and CIB. Methods: This separation is completed using a new, sparsity-based, parametric method which we refer to as premise. The method comprises of three main stages: 1) filtering of the raw data to reduce the effect of the CIB on the MBB fit. 2) fitting an MBB model to the filtered data across super-pixels of various sizes determined by the algorithm itself and 3) refining these super-pixel estimates into full resolution maps of the MBB parameters. Results: We present our maps of MBB temperature, spectral index and optical depth at 5 arcmin resolution and compare our estimates to those of GNILC as well as the two-step MBB fit presented by the Planck collaboration in 2013. Conclusions: By exploiting sparsity we avoid the need for smoothing, enabling us to produce the first full resolution MBB parameter maps from intensity measurements of thermal dust emission.We consider the premise parameter estimates to be competitive with the existing state-of-the-art solutions, outperforming these methods within low signal-to-noise regions as we account for the CIB without removing thermal dust emission through over-smoothing.

astro-ph.CO↗