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We present and compare several methods to mitigate time-correlated (1/f) noise within the HI intensity mapping component of the MeerKAT Large Area Synoptic Survey (MeerKLASS). By simulating scan strategies, the HI signal, white and correlated noise, we assess the ability of various data processing pipelines to recover the power spectrum of HI 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 16 and 23 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 1/f noise but find that over-cleaning results in the removal of HI power at large (angular and radial) scales; a power loss of 20 per cent is seen for a 2-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., 13 pages, 11 figures, submitted to MNRAS

Blind cleaning methods are currently the preferred strategy for handling foreground contamination in single-dish H I intensity mapping surveys. Despite the increasing sophistication of blind techniques, some signal loss will be inevitable across all scales. Constructing a corrective transfer function using mock signal injection into the contaminated data has been a practice relied on for H I intensity mapping experiments. However, assessing whether this approach is viable for future intensity mapping surveys, where precision cosmology is the aim, remains unexplored. In this work, using simulations, we validate for the first time the use of a foreground transfer function to reconstruct power spectra of foreground-cleaned low-redshift intensity maps and look to expose any limitations. We reveal that even when aggressive foreground cleaning is required, which causes >50 per cent negative bias on the largest scales, the power spectrum can be reconstructed using a transfer function to within sub-per cent accuracy. We specifically outline the recipe for constructing an unbiased transfer function, highlighting the pitfalls if one deviates from this recipe, and also correctly identify how a transfer function should be applied in an autocorrelation power spectrum. We validate a method that utilizes the transfer function variance for error estimation in foreground-cleaned power spectra. Finally, we demonstrate how incorrect fiducial parameter assumptions (up to ±100 per cent bias) in the generation of mocks, used in the construction of the transfer function, do not significantly bias signal reconstruction or parameter inference (inducing, Monthly Notices of the Royal Astronomical Society, 523 (2), ISSN:0035-8711, ISSN:1365-2966, ISSN:1365-8711

We study the clustering of HI intensity maps produced from simulations with a focus on baryonic acoustic oscillations (BAO) and the effects induced by telescope beam smoothing and foreground cleaning. We start by creating a HI catalogue at $z=1.321$ based on the Semi-Analytic Galaxy Evolution (SAGE) model applied to the UNIT simulations. With this catalogue we investigate the relation between model HI and the dark matter haloes and we also study the abundance of HI, $\Omega_{\rm HI}$, predicted by this model. We then create synthetic HI intensity maps with a Nearest-Grid-Point approach. In order to simulate the telescope beam effect, a Gaussian smoothing is applied on the plane perpendicular to the line of sight. The effect of foreground removal methods is simulated by exponentially damping the largest wavelength Fourier modes on the radial direction. We study the anisotropic 2-point correlation function (2PCF) $\xi(r_\perp,r_\parallel)$ and how it is affected by the aforementioned observational effects. In order to better isolate the BAO signal, we study several 2PCF $\mu$-wedges (with a restricted range of orientations $\mu$) tailored to address the systematics effects and we compare them with different definitions of radial 2PCFs. Finally, we discuss our findings in the context of an SKA-like survey, finding a clear BAO signal in most of the estimators here proposed., Comment: published in MNRAS; 18 pages, 10 figures

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., 21 pages, 16 figures. Changes made in response to reviewer report. Noted changes from V1: analysis now only performed on two foreground regions (excluding Galactic Plane and Barnard's loop since these were very high emission regions). General results and conclusions unchanged though. Version 2 accepted for publication in MNRAS

We present the joint analysis of Neutral Hydrogen (H I) Intensity Mapping observations with three galaxy samples: the Luminous Red Galaxy (LRG) and Emission Line Galaxy (ELG) samples from the eBOSS survey, and the WiggleZ Dark Energy Survey sample. The H I intensity maps are Green Bank Telescope observations of the redshifted 21cm emission on 100 deg(2) covering the redshift range 0.6 < z < 1.0. We process the data by separating and removing the foregrounds present in the radio frequencies with FAsTI ICA. We verify the quality of the foreground separation with mock realizations, and construct a transfer function to correct for the effects of foreground removal on the H I signal. We cross-correlate the cleaned H I data with the galaxy samples and study the overall amplitude as well as the scale dependence of the power spectrum. We also qualitatively compare our findings with the predictions by a semianalytical galaxy evolution simulation. The cross-correlations constrain the quantity Omega(H I)b(H I)r(H I,opt) at an effective scale k(eff), where Omega(H I) is the H I density fraction, b(H I )is the H I bias, and r(H I,opt) the galaxy-hydrogen correlation coefficient, which is dependent on the H I content of the optical galaxy sample. At k(eff) = 0.31 h Mpc(-1) we find Omega(H I)b(H I)r(H I,wig) = [0.58 +/- 0.09 (stat) +/- 0.05 (sys)] x 10(-3) for GBT-WiggleZ, Omega(H I)b(H I)r(H I,ELG) = [0.40 +/- 0.09 (stat) +/- 0.04 (sys)] x 10(-3) for GBT-ELG, and Omega(H I)b(H I)r(H I,LRG) = [0.35 +/- 0.08 (stat) +/- 0.03 (sys)] x 10(-3) for GBT-LRG, at z similar or equal to 0.8. We also report results at k(eff) = 0.24 and k(eff) = 0.48 h Mpc(-1). With little information on H I parameters beyond our local Universe, these are amongst the most precise constraints on neutral hydrogen density fluctuations in an underexplored redshift range.