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Superclusters and galaxy clusters offer a wide range of astrophysical science topics with regards to studying the evolution and distribution of galaxies, intra-cluster magnetization mediums, cosmic ray accelerations and large scale diffuse radio sources all in one observation. Recent developments in new radio telescopes and advanced calibration software have completely changed data quality that was never possible with old generation telescopes. Hence, radio observations of superclusters are a very promising avenue to gather rich information of a large-scale structure (LSS) and their formation mechanisms. These newer wide-band and wide field-of-view (FOV) observations require state-of-the-art data analysis procedures, including calibration and imaging, in order to provide deep and high dynamic range (DR) images with which to study the diffuse and faint radio emissions in supercluster environments. Sometimes, strong point sources hamper the radio observations and limit the achievement of a high DR. In this paper, we have shown the DR improvements around strong radio sources in the MeerKAT observation of the Saraswati supercluster by applying newer third-generation calibration (3GC) techniques using CubiCal and killMS software. We have also calculated the statistical parameters to quantify the improvements around strong radio sources. This analysis advocates for the use of new calibration techniques to maximize the scientific returns from new-generation telescopes.

Dying radio galaxies represent a stage of the evolution of active galactic nuclei (AGN), during which the accreting central black hole has switched off and/or falls to such a low level that the plasma outflow can no longer be sustained. When this happens, the radio source undergoes a period of fading, the dying phase, before it disappears completely. We present the study of three potential dying radio sources using the MeerKAT radio telescope: MKT J072851.2-752743, MKT J001940.4-654722, and ACO 548B. The identification as dying radio sources came from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS). We carry out a multi-wavelength analysis of the sources and derive their energetics. The ages of the sources are ∼30–70 Myr, they have magnetic fields of the order of a few μG, and they have relatively low radio power. Their potential optical counterparts are associated with massive galaxies. We show that ACO 548B, previously classified as two peripheral relic radio sources, is a dying radio galaxy. With its good sensitivity and resolution, MeerKAT is an ideal instrument to detect potential dying radio sources, and contribute to the understanding of the evolution of AGN population.

We present the results from a search for high-redshift radio galaxy (HzRG) candidates using 1.28 GHz data in the Abell 2751 field drawn from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS). We used the HzRG criteria that a radio source is undetected in all-sky optical and infrared catalogues and that it has a very steep radio spectrum. We used the likelihood ratio method for cross-matching the radio catalogue against multi-wavelength galaxy catalogues from the Dark Energy Camera Legacy Survey (DECaLS) and the All-sky Wide Infrared Survey Explorer (AllWISE). For those radio sources with no multi-wavelength counterpart, we further implemented a radio spectral index criterium of α<−1, using in-band spectral index measurements from the wide-band MeerKAT data. Using a 5σ signal-to-noise cut on the radio flux densities, we found a total of 274 HzRG candidates: 179 ultra-steep spectrum sources and 95 potential candidates, which could not be ruled out as they had no spectral information available. The spectral index assignments in this work were complete above a flux density of 0.3 mJy, which is at least an order of magnitude lower than existing studies in this frequency range or when extrapolating from lower frequency limits. Our faintest HzRG candidates with and without an in-band spectral index measurement had a 1.28 GHz flux density of 57 ± 8 μJy and 68 ± 13 μJy, respectively. Although our study is not complete down to these flux densities, our results indicate that the sensitivity and bandwidth of the MGCLS data make them a powerful radio resource to search for HzRG candidates in the Southern sky, with 20 of the MGCLS pointings having similar image quality as the Abell 2751 field and full coverage in both DECaLS and AllWISE. Data at additional radio frequencies will be needed for the faintest source populations, which could be provided in the near future by the MeerKAT UHF band (580–1015 MHz) at a similar resolution (∼8–10″).

In our study, we show a multiwavelength view of ACT-CL J0019.6+0336 (which hosts a radio halo), to investigate the cluster dynamics, morphology, and ICM. We use a combination of XMM-Newton images, Dark Energy Survey (DES) imaging and photometry, SDSS spectroscopic information, and 1.16 GHz MeerKAT data to study the cluster properties. Various X-ray and optical morphology parameters are calculated to investigate the level of disturbance. We find disturbances in two X-ray parameters and the optical density map shows elongated and axisymmetric structures with the main cluster component southeast of the cluster centre and another component northwest of the cluster centre. We also find a BCG offset of ∼950 km/s from the mean velocity of the cluster, and a discrepancy between the SZ mass, X-ray mass, and dynamical mass (MX,500 and MSZ,500 lies >3σ away from Mdyn,500), showing that J0019 is a merging cluster and probably in a post-merging phase.

The hypothesis that bent radio sources are supposed to be found in rich, massive galaxy clusters and the avalibility of huge amount of data from radio surveys have fueled our motivation to use Machine Learning (ML) to identify bent radio sources and as such use them as tracers for galaxy clusters. The shapelet analysis allowed us to decompose radio images into 256 features that could be fed into the ML algorithm. Additionally, ideas from the field of neuro-psychology helped us to consider training the machine to identify bent galaxies at different orientations. From our analysis, we found that the Random Forest algorithm was the most effective with an accuracy rate of 92% for a classification of point and extended sources as well as an accuracy of 80% for bent and unbent classification.

Radio astronomy is a specialised area of astronomy that examines the radio emissions from astronomical bodies within the electromagnetic spectrum's radio range. As radio telescopes have become increasingly sensitive due to technological advancements, radio astronomers face the significant challenge of reducing the impact of human-generated radio interference. Our research delved into the impact of Global System for Mobile Communication (GSM) signals on radio astronomy data, utilising a multidimensional framework approach with a probabilistic basis. We discovered a link between the location of cell towers in the nearby towns surrounding MeerKAT and a high probability of Radio Frequency Interference (RFI). However, we found no statistically significant association between the time of day and RFI occurrence at the 68% confidence level.

Radio frequency interference (RFI) has plagued radio astronomy which potentially might be as bad or worse by the time the Square Kilometre Array (SKA) comes up. RFI can be either internal (generated by instruments) or external that originates from intentional or unintentional radio emission generated by man. With the huge amount of data that will be available with up coming radio telescopes, an automated aproach will be required to detect RFI. In this paper to try automate this process we present the result of applying machine learning techniques to cross match RFI from the Karoo Array Telescope (KAT-7) data. We found that not all the features selected to characterise RFI are always important. We further investigated 3 machine learning techniques and conclude that the Random forest classifier performs with a 98% Area Under Curve and 91% recall in detecting RFI.

In our study, we show a multiwavelength view of ACT-CL J0019.6+0336 (which hosts aradio halo), to investigate the cluster dynamics, morphology, and ICM. We use a combination ofXMM-Newton images, Dark Energy Survey (DES) imaging and photometry, SDSS spectroscopicinformation, and 1.16 GHz MeerKAT data to study the cluster properties. Various X-ray and opticalmorphology parameters are calculated to investigate the level of disturbance. We find disturbancesin two X-ray parameters and the optical density map shows elongated and axisymmetric structureswith the main cluster component southeast of the cluster centre and another component northwest ofthe cluster centre. We also find a BCG offset of∼950 km/s from the mean velocity of the cluster, anda discrepancy between the SZ mass, X-ray mass, and dynamical mass (MX,500andMSZ,500lies>3σaway fromMdyn,500), showing that J0019 is a merging cluster and probably in a post-merging phase.

Radio Frequency Interference (RFI) is unwanted noise that swamps the desired astronomical signal. Radio astronomers have always had to deal with RFI detection and excision around telescope sites, but little has been done to understand the full scope, nature and evolution of RFI in a unified way. We undertake this for the MeerKAT array using a probabilistic multidimensional framework approach focussing on UHF-band and L-band data. In the UHF- band, RFI is dominated by the allocated Global System for Mobile (GSM) Communications, flight Distance Measuring Equipment (DME), and UHF-TV bands. The L-band suffers from known RFI sources such as DMEs, GSM, and the Global Positioning System (GPS) satellites. In the \"clean\" MeerKAT band, we noticed the RFI occupancy changing with time and direction for both the L-band and UHF band. For example, we saw a significant increase (300% increase) in the fraction of L-band flagged data in November 2018 compared to June 2018. This increase seems to correlate with construction activity on site. In the UHF-band, we found that the early morning is least impacted by RFI and other outliers. We also found a dramatic decrease in DME RFI during the hard lockdown due to the COVID-19 pandemic. The work presented here allows us to characterise the evolution of RFI at the MeerKAT site. Any observatory can adopt it to understand the behaviour of RFI within its surroundings.

Radio interferometry calibration and Radio Frequency Interference (RFI) removal are usually done separately. Here we show that jointly modelling the antenna gains and RFI has significant benefits when the RFI follows precise trajectories, such as for satellites. One surprising benefit is improved calibration solutions, by leveraging the RFI signal itself. We present tabascal (TrAjectory BAsed RFI Subtraction and CALibration), a new algorithm that jointly models the RFI and calibration parameters in visibilities. We test tabascal on simulated MeerKAT calibration observations contaminated by satellite-based RFI. We obtain gain estimates that are both unbiased and up to an order of magnitude better constrained compared to uncontaminated data. When combined with an ad hoc RFI subtraction scheme, tabascal solutions can be further applied to an adjacent target observation: 5 minutes of calibration data results in an image with about a third the noise achieved when using flagging alone. The recovered flux distribution of RFI subtracted data was on par with uncontaminated data. In contrast, RFI flagging alone resulted in a higher detection threshold and consistent underestimation of source fluxes. For a mean RFI amplitude of 17 Jy, using RFI subtraction leads to less than 1% loss of data compared to 75% data loss from an ideal \\(3\\sigma\\) flagging algorithm, a very significant increase in data available for science analysis. Although we have examined the case of satellite RFI, tabascal should work for any RFI moving on parameterizable trajectories, relative to the phase centre, such as planes and/or objects fixed to the ground.