As the largest radio telescope in the world, the Square Kilometre Array (SKA) will lead the next generation of radio astronomy. The feats of engineering required to construct the telescope array will be matched only by the techniques developed to exploit the rich scientific value of the data. To drive forward the development of efficient and accurate analysis methods, we are designing a series of data challenges that will provide the scientific community with high-quality data sets for testing and evaluating new techniques. In this paper, we present a description and results from the first such Science Data Challenge 1 (SDC1). Based on SKA MID continuum simulated observations and covering three frequencies (560, 1400, and 9200 MHz) at three depths (8, 100, and 1000 h), SDC1 asked participants to apply source detection, characterization, and classification methods to simulated data. The challenge opened in 2018 November, with nine teams submitting results by the deadline of 2019 April. In this work, we analyse the results for eight of those teams, showcasing the variety of approaches that can be successfully used to find, characterize, and classify sources in a deep, crowded field. The results also demonstrate the importance of building domain knowledge and expertise on this kind of analysis to obtain the best performance. As high-resolution observations begin revealing the true complexity of the sky, one of the outstanding challenges emerging from this analysis is the ability to deal with highly resolved and complex sources as effectively as the unresolved source population.

The emergence of supermassive black holes (SMBHs) in the early universe remains a topic of profound interest and debate. In this paper, we investigate the formation and growth of the first SMBHs within the framework of Modified Gravity (MOG), where gravity exhibits increased strength. We explore how MOG, as an alternative to the standard model, may offer novel insights into the emergence of SMBHs and potentially reconcile the discrepancies observed in the accretion and growth processes. We examine the dynamics of gas and matter in this modified gravitational framework, shedding light on the unique interplay between gravity and the formation of SMBHs.

We characterize the relaxation state of galaxy systems by providing an assessment of the reliability of the photometric and spectroscopic probe via the semianalytic galaxy evolution model. We quantify the correlations between the dynamical age of simuglated galaxy groups and popular proxies of halo relaxation in observation, which are mainly either spectroscopic or photometric. We find the photometric indicators demonstrate a stronger correlation with the dynamical relaxation of galaxy groups compared to the spectroscopic probes. We take advantage of the Anderson Darling statistic (A ²) and the velocity segregation ( ) as our spectroscopic indicators, and use the luminosity gap (Δm ₁₂) and the luminosity decentering (D _offset) as photometric ones. First, we find that a combination of Δm ₁₂ and D _offset evaluated by a bivariant relation ( ) shows a good correlation with the dynamical age compared to all other indicators. Second, by using the observational X-ray surface brightness map, we show that the bivariant relation brings about some acceptable correlations with X-ray proxies. These correlations are as well as the correlations between A ² and X-ray proxies, offering a reliable yet fast and economical method of quantifying the relaxation of galaxy systems. This study demonstrates that using photometric data to determine the relaxation status of a group will lead to some promising results that are comparable with the more expensive spectroscopic counterpart.

The discrepancy between expected and observed cooling rates of X-ray emitting gas has led to the cooling-flow problem at the cores of clusters of galaxies. A variety of models have been proposed to model the observed X-ray spectra and resolve the cooling-flow problem, which involves heating the cold gas through different mechanisms. As a result, realistic models of X-ray spectra of galaxy clusters need to involve both heating and cooling mechanisms. In this paper, we argue that the heating timescale is set by the magnetohydrodynamic (MHD) turbulent viscous heating for the Intracluster plasma, parameterized by the Shakura–Sunyaev viscosity parameter, α. Using a cooling+heating flow model, we show that a value of α ≃ 0.05 (with 10% scatter) provides improved fits to the X-ray spectra of cooling flow, while at the same time, predicting reasonable cooling efficiency, . Our inferred values for α based on X-ray spectra are also in line with direct measurements of turbulent pressure in simulations and observations of galaxy clusters. This simple picture unifies astrophysical accretion, as a balance of MHD turbulent heating and cooling, across more than 16 orders of magnitudes in scale, from neutron stars to galaxy clusters.

For monitoring the night sky conditions, wide-angle all-sky cameras are used in most astronomical observatories to monitor the sky cloudiness. In this manuscript, we apply a deep-learning approach for automating the identification of precipitation clouds in all-sky camera data as a cloud warning system. We construct our original training and test sets using the all-sky camera image archive of the Iranian National Observatory (INO). The training and test set images are labeled manually based on their potential rainfall and their distribution in the sky. We train our model on a set of roughly 2445 images taken by the INO all-sky camera through the deep learning method based on the EfficientNet network. Our model reaches an average accuracy of 99\% in determining the cloud rainfall's potential and an accuracy of 96\% for cloud coverage. To enable a comprehensive comparison and evaluate the performance of alternative architectures for the task, we additionally trained three models LeNet, DeiT, and AlexNet. This approach can be used for early warning of incoming dangerous clouds toward telescopes and harnesses the power of deep learning to automatically analyze vast amounts of all-sky camera data and accurately identify precipitation clouds formations. Our trained model can be deployed for real-time analysis, enabling the rapid identification of potential threats, and offering a scaleable solution that can improve our ability to safeguard telescopes and instruments in observatories. This is important now that numerous small and medium-sized telescopes are increasingly integrated with smart control systems to reduce manual operation.