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We present a novel analysis of observational systematics through the emission-line stacking of the MeerKLASS L-band deep-field intensity maps, following the detection in MeerKLASS Collaboration et al. A stacking signal is obtained by stacking the 21 cm intensity map cubelets around the galaxy positions from the GAMA survey at 0.39 ≲ z ≲ 0.46. An extensive simulation framework is built to study the viability of the stacking detection, the covariance estimation, and the model inference, which are then applied to the data. The statistical significance of the detection is 8.66σ when averaged into an angular map, and 7.45σ when averaged into a spectrum. The stacked spectrum exhibits an oscillating component of systematics, and we provide evidence that these systematics are a convolutional effect on the map data. The oscillation frequency matches the diffraction from the secondary reflector into the primary beam of the MeerKAT telescope. Bayesian inference can be used to constrain the systematics and the average H i emission of the galaxies. The fitting of the parameters gives a constraint on the systematics frequency νsys[MHz]=17.90−4.27+6.53 . The posterior of the systematics amplitude reaches the wide prior and gives Asys=0.50−0.33+0.33 . A tentative measurement of the average H i mass of the sources is achieved at log10[〈MHI〉/M⊙]=9.84−0.59+0.48 , which is an underestimation limited by the narrow redshift bin, the strong degeneracy with the systematics, and the low-density galaxy sample. These shortfalls will be resolved for future MeerKLASS data to enable accurate measurements of the H i density through stacking of intensity maps.

We present an all-sky map of the synchrotron spectral index and curvature between 45 and 2300 MHz at a resolution of 1 degree calculated from a combination of numerous partial sky empirical measurements. We employ a least-squares parametric fit which relies on removing a free-free emission template and a component separation technique which fits for both synchrotron and free-free emission. We compare our diffuse sky model estimates against those derived from the models widely used in the community (e.g. pysm3 and GSM) employing external datasets that were not included in the estimation process. Our evaluation focuses on identifying the enhanced consistency at both the map level and in pixel-to-pixel correlations, allowing for a more robust verification of our model's performance. We find our parametric, least-squares synchrotron estimate to be the most reliable across radio frequencies as it consistently provides sky models with average accuracies (when compared to empirical data) of around 20 per cent, whilst other model performances range on average between 10 and 70 per cent accurate. The results obtained have been made publicly accessible online and can be utilized to further develop and refine models of Galactic synchrotron emission.

The 408 MHz Haslam map is widely used as a low-frequency anchor for the intensity and morphology of Galactic synchrotron emission. Multi-frequency, multi-experiment fits show evidence of spatial variation and curvature in the synchrotron frequency spectrum, but there are also poorly-understood multiplicative flux scale disagreements between experiments. We perform a Bayesian model comparison across a range of scenarios, using fits that include recent spectroscopic observations at \\( 1\\) GHz by MeerKAT as well as a reference map from the OVRO-LWA at 73 MHz. In the few square degrees that we analyzed, a large uncorrected flux scale factor potentially as large as 1.6 in the Haslam data is preferred, indicating a 60\\% overestimation of the brightness. This partly undermines its use as a reference map. We also find that models with nonzero spectral curvature are statistically disfavored. Given the limited sky coverage here, we suggest a similar analysis across many more regions of the sky to determine the extent and variation of flux scale errors, and whether they should be treated as random or systematic errors in analyses that use the Haslam map as a template.

We present an all-sky map of the synchrotron spectral index and curvature between 45 and 2300 MHz at a resolution of 1 degree calculated from a combination of numerous partial sky empirical measurements. We employ a least-squares parametric fit which relies on removing a free-free emission template and a component separation technique which fits for both synchrotron and free-free emission. We compare our diffuse sky model estimates against those derived from the models widely used in the community (e.g. pysm3 and GSM) employing external datasets that were not included in the estimation process. Our evaluation focuses on identifying the enhanced consistency at both the map level and in pixel-to-pixel correlations, allowing for a more robust verification of our model's performance. We find our parametric, least-squares synchrotron estimate to be the most reliable across radio frequencies as it consistently provides sky models with average accuracies (when compared to empirical data) of around 20 per cent, whilst other model performances range on average between 10 and 70 per cent accurate. The results obtained have been made publicly accessible online and can be utilized to further develop and refine models of Galactic synchrotron emission.

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.

Removing contaminants is a delicate, yet crucial step in neutral hydrogen (HI) intensity mapping and often considered the technique's greatest challenge. Here, we address this challenge by analysing HI intensity maps of about \\(100\\) deg\\(^2\\) at redshift \\(z0.4\\) collected by the MeerKAT radio telescope, an SKA Observatory (SKAO) precursor, with a combined 10.5-hour observation. Using unsupervised statistical methods, we removed the contaminating foreground emission and systematically tested, step-by-step, some common pre-processing choices to facilitate the cleaning process. We also introduced and tested a novel multiscale approach: the data were redundantly decomposed into subsets referring to different spatial scales (large and small), where the cleaning procedure was performed independently. We confirm the detection of the HI cosmological signal in cross-correlation with an ancillary galactic data set, without the need to correct for signal loss. In the best set-up we achieved, we were able to constrain the HI distribution through the combination of its cosmic abundance (\\(_HI\\)) and linear clustering bias (\\(b_HI\\)) up to a cross-correlation coefficient (\\(r\\)). We measured \\(_HIb_HIr = [0.93 0.17]\\,\\,10^-3\\) with a \\(6\\) confidence, which is independent of scale cuts at both edges of the probed scale range (\\(0.04 k 0.3 \\,h\\) Mpc\\(^-1\\)), corroborating its robustness. Our new pipeline has successfully found an optimal compromise in separating contaminants without incurring a catastrophic signal loss. This development instills an added degree of confidence in the outstanding science we can deliver with MeerKAT on the path towards HI intensity mapping surveys with the full SKAO.

We present a novel analysis of observational systematics through the emission-line stacking of the MeerKLASS L-band deep-field intensity maps, following the detection in arXiv:2407.21626. A stacking signal is obtained by stacking the 21cm intensity map cubelets around the galaxy positions from the GAMA survey at \\(0.39 z 0.46\\). An extensive simulation framework is built to study the viability of the stacking detection, the covariance estimation, and the model inference, which are then applied to the data. The statistical significance of the detection is \\(8.66\\) when averaged into an angular map, and \\(7.45\\) when averaged into a spectrum. The stacked spectrum exhibits an oscillating component of systematics, and we provide evidence that these systematics are a convolutional effect on the map data. The oscillation frequency matches the diffraction from the secondary reflector into the primary beam of the MeerKAT telescope. Bayesian inference can be used to constrain the systematics and the average HI emission of the galaxies. The fitting of the parameters gives a constraint on the systematics frequency \\(_ sys\\,[ MHz] = 17.90^+6.53_-4.27\\). The posterior of the systematics amplitude reaches the wide prior and gives \\(A_ sys=0.50^+0.33_-0.33\\). A tentative measurement of the average HI mass of the sources is achieved at \\(_10[ M_HI/M_ ]=9.84^+0.48_-0.59\\), which is an underestimation limited by the narrow redshift bin, the strong degeneracy with the systematics, and the low-density galaxy sample. These shortfalls will be resolved for future MeerKLASS data to enable accurate measurements of the HI density through stacking of intensity maps.

Global 21cm experiments aim to detect the hydrogen 21cm signal by separating it from foreground emission that can be orders of magnitude brighter than the signal. REACH (the Radio Experiment for the Analysis of Cosmic Hydrogen) forward-models the sky by jointly fitting signal and foreground spectral parameters to an existing sky map. The fitted parameters yield spectrally constrained, absolutely calibrated maps of the radio sky across the full 50-170 MHz observing band, among the lowest continuous frequencies yet mapped. We assess REACH's ability to fit the 21cm signal and recover accurate foreground maps, using physically motivated foreground models of increasing complexity (starting from a pure synchrotron power law model, then introducing variable amplitudes, curvature, and a free-free component). We evaluate these models against simulated REACH observations of correspondingly complex foregrounds, based on the Global Sky Model and the Python Sky Model. To recover the 21cm signal, more complex datasets require correspondingly complex models, but this introduces degeneracies which limit accurate recovery of foreground parameters. Fitting a foreground with independent synchrotron and free-free emission enables component-separated sky mapping, which has applications beyond radio cosmology; synchrotron is well-recovered across the sky, but free-free recovery is limited. REACH is therefore capable of probing Galactic physics at uniquely low frequencies, alongside its primary goal of detecting the 21cm signal.

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 \\( 5\\%\\) for \\(>25\\) (\\(20 - 30 \\%\\) for \\(k_ 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.

The 408 MHz Haslam map is widely used as a low-frequency anchor for the intensity and morphology of Galactic synchrotron emission. Multi-frequency, multi-experiment fits show evidence of spatial variation and curvature in the synchrotron frequency spectrum, but there are also poorly-understood gain factors between experiments. We perform a Bayesian model comparison across a range of scenarios, using fits that include recent spectroscopic observations at \\( 1\\)~GHz by MeerKAT. A large uncorrected gain factor of about 60\\% in the Haslam data is strongly preferred, partly undermining its use as a reference template.