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Estimators for n -point clustering statistics in Fourier-space demand that modern surveys of large-scale structure be transformed to Cartesian coordinates to perform Fast Fourier Transforms (FFTs). In this work, we explore this transformation in the context of pixelized line intensity maps (LIM), highlighting potential biasing effects on power-spectrum measurements. Current analyses often avoid a complete resampling of the data by approximating survey geometry as rectangular in Cartesian space, an increasingly inaccurate assumption for modern wide-sky surveys. Our simulations of a |$20\, {\times }\, 20\, \text{deg}^2$| 21 cm LIM survey at |$0.34\, {\lt }\, z\, {\lt }\, 0.54$| show this assumption biases power-spectrum measurements by |${\gt }\, 20~{{\ \rm per\ cent}}$| across all scales. We therefore present a more robust framework for regridding the voxel intensities on to a 3D FFT field by coordinate transforming large numbers of Monte-Carlo sampling particles. Whilst this unbiases power-spectrum measurements on large scales, smaller scale discrepancies remain, caused by structure smoothing and aliasing from separations unresolved by the grid. To correct these effects, we introduce modelling techniques, higher order particle assignments, and interlaced FFT grids to suppress the aliased power. Using a piecewise cubic spline (PCS) particle assignment and an interlaced FFT field, we achieve sub-per cent accuracy up to 80 per cent of the Nyquist frequency for our 21 cm LIM simulations. We find a more subtle hierarchical improvement in results for higher order assignment schemes, relative to the gains made for galaxy surveys, which we attribute to the extra complexity in LIM from additional discretizing steps. python code accompanying this paper is available at github.com/stevecunnington/gridimp. [ABSTRACT FROM AUTHOR]

We present a survey strategy to detect the neutral hydrogen (H  i) power spectrum at 5 < z < 6 using the SKA-Low radio telescope in presence of foregrounds and instrumental effects. We simulate observations of the inherently weak H  i signal post-reionization with varying levels of noise and contamination with foreground amplitudes equivalent to residuals after sky model subtraction. We find that blind signal separation methods on imaged data are required in order to recover the H  i signal at large cosmological scales. Comparing different methods of foreground cleaning, we find that Gaussian Process Regression (GPR) performs better than Principle Component Analysis (PCA), with the key difference being that GPR uses smooth kernels for the total data covariance. The integration time of one field needs to be larger than ∼250 h to provide large enough signal-to-noise ratio (SNR) to accurately model the data covariance for foreground cleaning. Images within the primary beam field-of-view give measurements of the H  i power spectrum at scales |$k\sim 0.02\, {\rm Mpc^{-1}}-0.3\, {\rm Mpc^{-1} }$| with SNR ∼2–5 in Δ[log(k /Mpc−1)] = 0.25 bins assuming an integration time of 600 h. Systematic effects, which introduce small-scale fluctuations across frequency channels, need to be ≲ 5 × 10−5 to enable unbiased measurements outside the foreground wedge. Our results provide an important validation towards using the SKA-Low array for measuring the H  i power spectrum in the post-reionization Universe. [ABSTRACT FROM AUTHOR]

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 |${\gt }\, 50~{{\ \rm per\ cent}}$| 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 |${\pm }100~{{\ \rm per\ cent}}$| bias) in the generation of mocks, used in the construction of the transfer function, do not significantly bias signal reconstruction or parameter inference (inducing |${\lt }\, 5~{{\ \rm per\ cent}}$| bias in recovered values). [ABSTRACT FROM AUTHOR]

We present a detection of correlated clustering between MeerKAT radio intensity maps and galaxies from the WiggleZ Dark Energy Survey. We find a 7.7σ detection of the cross-correlation power spectrum, the amplitude of which is proportional to the product of the |${\rm H}\, {\small I}$| density fraction (⁠|$\Omega _{{\rm H}\, {\small I}}$|⁠), |${\rm H}\, {\small I}$| bias (⁠|$b_{{\rm H}\, {\small I}}$|⁠), and the cross-correlation coefficient (r). We therefore obtain the constraint |$\Omega _{{\rm H}\, {\small I}}b_{{\rm H}\, {\small I}}r\, {=}\, [0.86\, {\pm }\, 0.10\, ({\rm stat})\, {\pm }\, 0.12\, ({\rm sys})]\, {\times }\, 10^{-3}$|⁠ , at an effective scale of |$k_{\rm eff}\ {\sim }\ 0.13\, h\, \text{Mpc}^{-1}$|⁠. The intensity maps were obtained from a pilot survey with the MeerKAT telescope, a 64-dish pathfinder array to the SKA Observatory (SKAO). The data were collected from 10.5 h of observations using MeerKAT 's L -band receivers over six nights covering the 11 h field of WiggleZ, in the frequency range 1015–973 MHz (0.400 |$\, {\lt }\, z\, {\lt }\,$| 0.459 in redshift). This detection is the first practical demonstration of the multidish autocorrelation intensity mapping technique for cosmology. This marks an important milestone in the roadmap for the cosmology science case with the full SKAO. [ABSTRACT FROM AUTHOR]

We conduct the first case study towards developing optimal foreground mitigation strategies for neutral hydrogen (H  i) intensity mapping using radio interferometers at low redshifts. A pipeline for simulation, foreground mitigation, and power spectrum estimation is built, which can be used for ongoing and future surveys using MeerKAT and Square Kilometre Array Observatory. It simulates realistic sky signals to generate visibility data-given instrument and observation specifications, which is subsequently used to perform foreground mitigation and power spectrum estimation. A quadratic estimator formalism is developed to estimate the temperature power spectrum in visibility space. Using MeerKAT telescope specifications for observations in the redshift range, |$z$| ∼ 0.25–0.30, corresponding to the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey, we present a case study, where we compare different approaches of foreground mitigation. We find that component separation in visibility space provides a more accurate estimation of H  i clustering when compared with foreground avoidance, with the uncertainties being 30 per cent smaller. Power spectrum estimation from image is found to be less robust with larger bias and more information loss when compared with estimation in visibility. We conclude that for the considered sub-band of |$z$| ∼ 0.25–0.30, the MIGHTEE survey will be capable of measuring the H  i power spectrum from |$k\sim 0.5$| to |$k\sim 10\, {\rm Mpc^{-1}}$| with signal-to-noise ratio being ∼3. We are the first to show that, at low redshift, component separation in visibility space suppresses foreground contamination at large line-of-sight scales, allowing measurement of H  i power spectrum closer to the foreground wedge, crucial for data analysis towards future detections. [ABSTRACT FROM AUTHOR]

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 |$21\rm cm$| emission on |$100 \, {\rm deg}^2$| 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 |$\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm opt}}$| at an effective scale k eff, where |$\Omega _\rm {H\,\small {I}}$| is the H   i density fraction, |$b_\rm {H\,\small {I}}$| is the H  i bias, and |$r_{\rm {H\,\small {I}},{\rm opt}}$| the galaxy–hydrogen correlation coefficient, which is dependent on the H   i content of the optical galaxy sample. At |$k_{\rm eff}=0.31 \, h\,{\rm Mpc^{-1}}$| we find |$\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm Wig}} = [0.58 \pm 0.09 \, {\rm (stat) \pm 0.05 \, {\rm (sys)}}] \times 10^{-3}$| for GBT-WiggleZ, |$\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm ELG}} = [0.40 \pm 0.09 \, {\rm (stat) \pm 0.04 \, {\rm (sys)}}] \times 10^{-3}$| for GBT-ELG, and |$\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm LRG}} = [0.35 \pm 0.08 \, {\rm (stat) \pm 0.03 \, {\rm (sys)}}] \times 10^{-3}$| for GBT-LRG, at z ≃ 0.8. We also report results at |$k_{\rm eff}=0.24$| and |$k_{\rm eff}=0.48 \, h\,{\rm Mpc^{-1}}$|⁠. 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. [ABSTRACT FROM AUTHOR]

We present a new halo model of neutral hydrogen (H  i) calibrated to galaxy formation simulations at redshifts z ∼ 0.1 and z ∼ 1.0 that we employ to investigate the constraining power of interferometric H  i intensity mapping on H  i astrophysics. We demonstrate that constraints on the small-scale H  i power spectrum can break the degeneracy between the H  i density |$\Omega _{\rm H\, \small {I}}$| and the H  i bias |$b_{\rm H\, \small {I}}$|⁠. For z ∼ 0.1, we forecast that an accurate measurement of |$\Omega _{\rm H\, \small {I}}$| up to 6 per cent level precision and the large-scale H  i bias |$b_{\rm H\, \small {I}}^0$| up to 1 per cent level precision can be achieved using Square Kilometre Array (SKA) pathfinder data from MeerKAT and Australian SKA Pathfinder (ASKAP). We also propose a new description of the H  i shot noise in the halo model framework in which a scatter of the relation between the H  i mass of galaxies and their host halo mass is taken into account. Furthermore, given the number density of H  i galaxies above a certain H  i mass threshold, future surveys will also be able to constrain the H  i mass function using only the H  i shot noise. This will lead to constraints at the 10 per cent level using the standard Schechter function. This technique will potentially provide a new way of measuring the H  i mass function, independent from existing methods. We predict that the SKA will be able to further improve the low-redshift constraints by a factor of 3, as well as pioneering measurements of H  i astrophysics at higher redshifts. [ABSTRACT FROM AUTHOR]

The future of precision cosmology could benefit from cross-correlations between intensity maps of unresolved neutral hydrogen (H  i) and more conventional optical galaxy surveys. A major challenge that needs to be overcome is removing the 21cm foreground emission that contaminates the cosmological H  i signal. Using N -body simulations, we simulate H  i intensity maps and optical catalogues that share the same underlying cosmology. Adding simulated foreground contamination and using state-of-the-art reconstruction techniques, we investigate the impacts that 21cm foregrounds and other systematics have on these cross-correlations. We find that the impact a Fast Independent Component Analysis 21cm foreground clean has on the cross-correlations with spectroscopic optical surveys with well-constrained redshifts is minimal. However, problems arise when photometric surveys are considered: We find that a redshift uncertainty σ z ≥ 0.04 causes significant degradation in the cross-power spectrum signal. We diagnose the main root of these problems, which relates to arbitrary amplitude changes along the line of sight in the intensity maps caused by the foreground clean and suggest solutions that should be applicable to real data. These solutions involve a reconstruction of the line-of-sight temperature means using the available overlapping optical data along with an artificial extension to the H  i data through redshift to address edge effects. We then put these solutions through a further test in a mock experiment that uses a clustering-based redshift estimation technique to constrain the photometric redshifts of the optical sample. We find that with our suggested reconstruction, cross-correlations can be utilized to make an accurate prediction of the optical redshift distribution. [ABSTRACT FROM AUTHOR]