Damghan University PressIranian Journal of Astronomy and Astrophysics2322-492412120250629On Equivalence of Definition of the Energy-Momentum Tensor in General Relativity and Field Theory1649110.22128/ijaa.2025.1025.1213ENSohrab RahvarDepartment of Physics, Sharif University of Technology, P.O. Box 11155-9161, Tehran, Iran0000-0002-7084-5725Journal Article20250607This work investigates a fundamental pedagogical question concerning the definition of the energy-momentum tensor in theoretical physics. Students learn the definition of energy-momentum tensor in the field theory and on the other hand a different definition of energy-momentum tensor in general relativity. Why these two definitions are equivalent. We present a proof establishing the equivalence between two seemingly distinct definitions: (1) the energy-momentum tensor is obtained from the Einstein-Hilbert action in General Relativity by varying Lagrangian of matter with respect to the metric field, and (2) the canonical energy-momentum tensor derived from Noether’s theorem in classical field theory where the Lagrangian vary with respect to the field. We use generic coordinate transformation where the fields and metric vary at the same time. Using the Noether theorem and setting the variation of action to zero, we bridge between these two approaches. Our analysis clarifies the conceptual link between geometric and field theory formulations of energy-momentum.https://ijaa.du.ac.ir/article_491_6410c63899d78bd74f55634f9bc5f181.pdfDamghan University PressIranian Journal of Astronomy and Astrophysics2322-492412120250806Dirac Stars Stability and Modified Einstein-Dirac-Maxwell Gravity736186010.22128/ijaa.2025.965.1207ENHossein GhaffarnejadFaculty of Physics, Semnan University, P.C. 35131-19111, Semnan, Iran0000-0002-0438-6452Tohid GhorbaniFaculty of Physics, Semnan University, P.C. 35131-19111, Semnan, IranJournal Article20250222To consider the cosmic magnetic effects on the rate of cosmic inflation, instead of unknown dark sector of matter/energy, some authors presented non-minimally coupled exotic Einstein-Maxwell (EM) gravity theories which we address in this work. We use one of these models and add Dirac action functional interacting with gauge Maxwell field to the exotic EM gravity and then investigate the formation and stability of a relativistic fermion star by using the dynamical system approach. Mathematical calculations predict important role of frequencies of the Dirac waves in formation of the Dirac star and whose critical energy density. Dirac star is a particular kind of fermionic relativistic star which has spherically symmetric static metric field. In this context, the directional interaction parameter between the gravity, and the electromagnetic fields play more important role, particularly in size of the star. Large values of that parameter makes larger Dirac star. Furthermore, we apply the dynamical system approach to find stabilization conditions of the Dirac star. These conditions are linked to specific values of the total angular momentum quantum numbers (including both spin and orbital contributions).https://ijaa.du.ac.ir/article_1860_2754cbb43ef46a7de6b6963fb5624273.pdfDamghan University PressIranian Journal of Astronomy and Astrophysics2322-492412120250826Applications of Image Processing Methods in Solar Coronal Data Analyzes: An Overview3771189010.22128/ijaa.2025.1013.1212ENMohsen JavaherianResearch Institute for Astronomy and Astrophysics of Maragha (RIAAM), University of Maragheh, Maragheh, P.O. Box: 55134-441, Iran0000-0003-0464-1561Seyed Alireza Bashiri MosaviDepartment of Electrical and Computer Engineering, Buein Zahra Technical University, Buein Zahra, Qazvin, Iran0000-0002-2540-8323Parsa MohammadiDepartment of Electrical and Computer Engineering, Buein Zahra Technical University, Buein Zahra, Qazvin, IranZahra EskandariDepartment of Geography and Rural Planning, University of Zanjan, University Blvd., 45371-38791, Zanjan, Islamic Republic of Iran0000-0002-8687-1143Journal Article20250517The solar corona, the outermost layer of the Sun's atmosphere, plays a crucial role in understanding solar phenomena and their impact on space weather and terrestrial systems. As advancements in solar observation technologies continue to produce high-resolution coronal images, the need for effective image processing methods has become increasingly important. This paper highlights the significance of processing techniques specifically designed for solar coronal images, which are essential for revealing intricate details of coronal structures, such as solar flares, coronal mass ejections, and magnetic field configurations. We explore various image processing methodologies, including event detection, contrast enhancement, and pattern analysis, that enhance the quality and interpretability of coronal images. Additionally, we address the challenges associated with processing coronal images, such as the presence of unrelated background, variability in data acquisition, and the complexity of coronal dynamics. By reviewing recent advancements in new instruments and algorithms, this paper explains how improved technologies and developed processing methods can lead to more accurate analyses of coronal phenomena, ultimately contributing to the broader field of solar research and our understanding of the Sun's influence on the solar system. The findings emphasize the need for ongoing innovation in image processing methods to unlock new insights into the behavior of the solar corona and its implications for space weather forecasting and solar-terrestrial interactions.https://ijaa.du.ac.ir/article_1890_00e570f176656b3b5f13cebd202f21cd.pdfDamghan University PressIranian Journal of Astronomy and Astrophysics2322-492412120250601Statistical Analysis of Magnetic Patches in Solar Active Regions using the Complex Network Method7384189210.22128/ijaa.2025.1022.1214ENZahra TajikDepartment of Physics, Faculty of Science, University of Zanjan, University Blvd., Zanjan, 45371-38791, Zanjan, Iran0000-0001-8453-1703Hasan RezaeiDepartment of Geography, Faculty of Basic Sciences, Imam Ali Nazaja University, Postal Code: 1317893471, Tehran, IranMehdi SimiariDepartment of Physics, Faculty of Science, Imam Ali University, Postal Code: 1317893471, Tehran, IranHossien SafariDepartment of Physics, Faculty of Science, University of Zanjan, University Blvd., Postal Code 45371-38791, Zanjan, Iran0000-0003-2326-3201Journal Article20250612Identifying solar active regions (ARs), which consist of one or more pairs of magnetic patches with opposite polarities, is essential due to their significant role in dynamic solar atmospheric phenomena such as solar flares and coronal mass ejections. In this study, we analyze ARs during their emergence and subsequent evolution on the solar surface using a novel complex network-based method known as Identifying Solar Magnetic Patches (ISMP). To examine the magnetic characteristics in detail, we selected a subregion of 125~$\times$~125 pixels centered on AR NOAA No., 1158, observed in 2011. Line-of-sight magnetogram data were obtained from the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). Our comprehensive statistical analysis reveals that the distributions of patch area, lifetime, and magnetic flux follow power-law behavior, with exponents approximately equal to $\alpha = 2.14$, 2.5, and 1.42, respectively. Furthermore, a calculated Hurst exponent of 0.57 indicates the presence of long-range temporal correlations in the emergence of new magnetic patches on the solar surface.https://ijaa.du.ac.ir/article_1892_f780a4d4e402277d4ae21ebe656452d8.pdfDamghan University PressIranian Journal of Astronomy and Astrophysics2322-492412120250601Machine Learning for Exoplanet Detection: A Comparative Analysis using Kepler Data8598189110.22128/ijaa.2025.2996.1219ENReihaneh KarimiSchool of Astronomy, Institute for Research in Fundamental Sciences (IPM), P. O. Box 19395-5531, Tehran, Iran0000-0003-2295-0499Mahdiyar Mousavi-SadrSchool of Astronomy, Institute for Research in Fundamental Sciences (IPM), P. O. Box 19395-5531, Tehran, Iran; Iranian National Observatory (INO), Institute for Research in Fundamental Sciences (IPM), P. O. Box 19568-36613, Tehran, Iran0000-0002-5170-2534Mohammad H. Zhoolideh HaghighiDepartment of Physics, K. N. Toosi University of Technology, P. O. Box 15875-4416, Tehran, Iran; School of Astronomy, Institute for Research in Fundamental Sciences (IPM), P. O. Box 19395-55310000-0001-5759-0302Fatemeh S. TabatabaeiSchool of Astronomy, Institute for Research in Fundamental Sciences (IPM), P. O. Box 19395-5531, Tehran, Iran0000-0002-0377-0970Journal Article20250720The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. In this study, we present a machine learning (ML) framework for exoplanet identification using a time-series photometric dataset from the Kepler Space Telescope, comprising 3,198 flux measurements across 5,074 stars. We investigate the performance of four supervised classification algorithms, namely Random Forest, k-Nearest Neighbors (KNN), Decision Tree, and Logistic Regression, using a comprehensive set of evaluation metrics such as accuracy, precision, recall, F1-score, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), confusion matrices, and learning curves. Among the models, Random Forest achieves the highest accuracy (99.8\%) and near-perfect F1-scores, demonstrating superior generalization and robustness. KNN also performs strongly, achieving 99.3\% accuracy, while Decision Tree demonstrates moderate performance with 97.1\% accuracy, and Logistic Regression trails behind with the lowest accuracy and generalization at 95.8\%. Notably, the application of the Synthetic Minority Over-sampling Technique (SMOTE) significantly improves performance across all models by addressing class imbalance. These findings underscore the effectiveness of ensemble-based machine learning techniques, particularly Random Forest, in handling large volumes of photometric data for automated exoplanet detection. This approach holds significant potential for implementation at ground-based facilities, such as the Iranian National Observatory (INO), where such extensive and precise datasets can further advance exoplanet discovery and characterization efforts.https://ijaa.du.ac.ir/article_1891_9c9d1180aa16d1b2b801949cffdbbb6b.pdfDamghan University PressIranian Journal of Astronomy and Astrophysics2322-492412120250916Observational Insights into Pre-Flare Very Large Pulsations in the Solar Corona99109190110.22128/ijaa.2025.2993.1217ENNarges FathalianPhysics, Basic Sciences, Department, Payame Nour University(PNU), Tehran, Iran0000-0003-2824-1773Malihe JalaliradPhysics, Basic Sciences Department, Payame Nour University, Tehran, IranJournal Article20250716Solar flares are impulsive releases of magnetic energy in the Sun’s atmosphere, producing broadband electromagnetic radiation from radio waves through optical and ultraviolet up to X- and gamma-ray photons. Their intensity is quantified by the peak soft X-ray flux in the range of 1 – 8 {\AA} as measured by the GOES satellites, and thus classified—on a logarithmic scale—into A, B, C, M, and X classes, each representing a tenfold increase in flux. Accurate forecasting of both the timing and intensity class of solar flares is essential for mitigating their potentially severe impacts. Among the most promising precursors are very-long-period pulsations (VLPs)—broadband oscillations with reported periods of roughly 8–30 minutes that typically emerge one to two hours before flare onset. In this study, we analyzed GOES 1–8 {\AA} soft X-ray data of three new flares and detected distinctive pre-flare VLP signatures in two M-class events. Measured pulsation periods detected are 25.2 and 20.0 (mixed with a period of 43 minutes), and each event exhibited four to six discrete pulses. Our analysis showed that increasing the number of free parameters in the fitting function can significantly enhance the accuracy of the flux model, as evidenced by improved goodness-of-fit metrics. The remaining third flare in our sample showed no clear VLP activity in the pre-eruption interval. https://ijaa.du.ac.ir/article_1901_dada9b0e21240faf1e8d0129c35b4d77.pdf