Mohammad Hossein Zhoolideh Haghighi

Gravitational microlensing of gamma-ray bursts (GRBs) provides a unique opportunity to probe compact dark matter and small-scale structures in the Universe. However, identifying such microlensed GRBs within large data sets is a significant challenge. In this study, we develop a machine learning (ML) approach to distinguish lensed GRBs from their nonlensed counterparts, using simulated light curves. A comprehensive data set is generated, comprising labeled light curves for both categories. Features are extracted using the Cesium package, capturing critical temporal properties of the light curves. Multiple ML models are trained on the extracted features, with Random Forest achieving the best performance, delivering an accuracy of 86% and an F₁ score of 0.86 (0.87) for the nonlensed (lensed) class. This approach successfully demonstrates the potential of ML for identifying gravitational lensing in GRBs, paving the way for future observational applications.

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.

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.

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.

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.

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.

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.

We study the statistical mechanics of binary systems under the 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 the physical parameters of the binary system.