Steven Cunnington

Removing contaminants is a delicate, yet crucial step in neutral hydrogen (H I ) intensity mapping and often considered the technique’s greatest challenge. Here, we address this challenge by analysing H I intensity maps of about 100 deg ² at redshift z ≈ 0.4 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 H I 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 H I distribution through the combination of its cosmic abundance (Ω _{H  I} ) and linear clustering bias ( b _{H  I} ) up to a cross-correlation coefficient ( r ). We measured Ω _{H  I} b _{H  I} r = [0.93 ± 0.17] × 10 ⁻³ with a ≈6 σ 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 H I intensity mapping surveys with the full SKAO.

We present the calibration procedures and validation of source measurement with the data of the Commensal Radio Astronomy FAST Survey for H i intensity mapping by the Five-hundred-meter Aperture Spherical Radio Telescope. Using a 70 hr drift-scan observation with the L-band (1.05–1.45 GHz) 19 beam receiver, we obtain the data covering a 270 deg² sky area. We employ both the pulsar backend and the spectrum backend to calibrate the spectral time-ordered data (TOD) before projecting them onto HEALPix maps. We produce calibrated TOD with a frequency resolution of 30 kHz and time resolution of 1 s and the map data cube with a frequency resolution of 30 kHz and spatial resolution of 2.95 arcmin². We examine the pointing errors, noise overflow, radio-frequency interference (RFI) contamination, and their effect on the data quality. The resulting noise level is ∼5.7 mJy for the calibrated TOD and 1.6 mJy for the map, consistent with the theoretical predictions within 5% at RFI-free channels. We also validate the data by principal component analysis and find that the residual map looks thermal noise dominated after removing 30 modes. We identify 447 isolated bright continuum sources in our data matching the NRAO VLA Sky Survey catalog, with relative flux error of 8.3% for TOD and 6.6% for the map level. We also measure the H i emission of 90 galaxies with redshift z < 0.07 and compare them with H i-MaNGA spectra, yielding an overall relative H i integral flux error of 16.7%. These results provide an important first step in assessing the feasibility of conducting cosmological H i detection with CRAFTS.

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 ν sys [ MHz ] = 17.9 0 − 4.27 + 6.53 . The posterior of the systematics amplitude reaches the wide prior and gives A sys = 0.5 0 − 0.33 + 0.33 . A tentative measurement of the average H i mass of the sources is achieved at log 10 [ 〈 M H I 〉 / M ⊙ ] = 9.8 4 − 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.

Radio frequency interference (RFI) is emitted from various sources, terrestrial or orbital, and creates a nuisance for ground-based 21-cm experiments. In particular, single-dish observations will be highly susceptible to RFI due to their wide primary beam and sensitivity. This work aimed to simulate the contamination effects from the Radio Navigational Satellite System (RNSS) within the 1100–1350 (MHz) frequency band. The simulation can be divided into two parts: (i) satellite positioning, emission power, and the beam response on the telescope, and (ii) calibration of the satellite signals to data to improve the original model. We utilize previously observed single-dish L-band data from the Meer-Karoo Array Telescope (MeerKAT), which requires special calibration to account for regions contaminated by satellite-based RFI. We find that we can recreate the satellite contamination with high accuracy around its peak frequencies provided the satellite is not too close to the telescope’s pointing direction. The simulation can predict satellite movements and signals for past and future observations, aiding in RFI avoidance and testing novel cleaning methods. The predicted signal sits below the noise in the target cosmology window in the L band (970–1015 MHz) making it difficult to confirm any out-of-band emission from satellites. However, in our simulations, this contamination still overwhelmed the 21-cm auto-power spectrum. Nevertheless, it is possible to detect the signal in cross-correlations after mild foreground cleaning. Whether such out of band contamination does exist will require further characterization of the satellite signals far away from their peak frequencies.

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, foreground emissions, 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 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 Hi 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.