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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., Comment: It has been accepted for publication by European Physical Journal C

Classification is a popular task in the field of Machine Learning (ML) and Artificial Intelligence (AI), and it happens when outputs are categorical variables. There are a wide variety of models that attempts to draw some conclusions from observed values, so classification algorithms predict categorical class labels and use them in classifying new data. Popular classification models including logistic regression, decision tree, random forest, Support Vector Machine (SVM), multilayer perceptron, Naive Bayes, and neural networks have proven to be efficient and accurate applied to many industrial and scientific problems. Particularly, the application of ML to astronomy has shown to be very useful for classification, clustering, and data cleaning. It is because after learning computers, these tasks can be done automatically by them in a more precise and more rapid way than human operators. In view of this, in this paper, we will review some of these popular classification algorithms, and then we apply some of them to the observational data of nonvariable and the RR Lyrae variable stars that come from the SDSS survey. For the sake of comparison, we calculate the accuracy and F1-score of the applied models., Comment: Proceedings based on the lectures given at the hands-on workshop of the ICRANet-ISFAHAN Astronomy Meeting, to be published in Astronomical and Astrophysical Transactions

This article is based on the tutorial we gave at the hands-on workshop of the ICRANet-ISFAHAN Astronomy Meeting. We first introduce the basic theory of machine learning and sort out the whole process of training a neural network. We then demonstrate this process with an example of inferring redshifts from SDSS spectra. To emphasize that machine learning for astronomy is easy to get started, we demonstrate that the most basic CNN network can be used to obtain high accuracy, we also show that with simple modifications, the network can be converted for classification problems and also to processing gravitational wave data., Comment: Proceedings based on the lectures given at the hands-on workshop of the ICRANet-ISFAHAN Astronomy Meeting, to be published in Astronomical and Astrophysical Transactions

We study the statistical mechanics of binary systems under gravitational interaction of the Modified Newtonian Dynamics (MOND) in three-dimensional space. Considering the binary systems, in the microcanonical and canonical ensembles, we show that in the microcanonical systems, unlike the Newtonian gravity, there is a sharp phase transition, with a high-temperature homogeneous phase and a low temperature clumped binary one. Defining an order parameter in the canonical systems, we find a smoother phase transition and identify the corresponding critical temperature in terms of physical parameters of the binary system., Comment: 14 pages, 5 figures

We characterize the relaxation state of galaxy systems by providing an assessment of the reliability of the photometric and spectroscopic probe via the semi-analytic galaxy evolution model. We quantify the correlations between the dynamical age of simulated 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 ($\bar{\Delta V}$) as our spectroscopic indicators, and use the luminosity gap ($\Delta m_{12}$) and the luminosity de-centring ($D_{\mathrm{off-set}}$) as photometric ones. Firstly, we find that a combination of $\Delta m_{12}$ and $D_{\mathrm{off-set}}$ evaluated by a bivariant relation ($\mathrm{B} = 0.04 \times \Delta m_{12} - 0.11 \times Log(D_{off-set}) + 0.28$), shows a good correlation with the dynamical age compared to all other indicators. Secondly, 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 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., Comment: Accepted for publication in Astronomical Journal (ApJ)

The discrepancy between expected and observed cooling rates of X-ray emitting gas has led to the {\it 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 {\it and} cooling mechanisms. In this paper, we argue that the heating time-scale is set by the magnetohydrodynamic (MHD) turbulent viscous heating for the Intracluster plasma, parametrised by the Shakura-Sunyaev viscosity parameter, $\alpha$. Using a cooling+heating flow model, we show that a value of $\alpha\simeq 0.05$ (with 10\% scatter) provides improved fits to the X-ray spectra of cooling flow, while at the same time, predicting reasonable cooling efficiency, $\epsilon_{cool} = 0.33^{+0.63}_{-0.15}$. Our inferred values for $\alpha$ 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., Comment: Accepted for publication in The Astrophysical Journal