arXiv subjects

Reihaneh Karimi

Publications and source records attributed to Reihaneh Karimi.

4 recordsLinked to original sources

arXiv2026

Machine Learning-Based Classification of Active Galaxies and Estimation of Supermassive Black Hole Masses

Distinguishing active galaxies from star-forming galaxies is essential for understanding galaxy evolution. Diagnostic methods like the BPT (Baldwin, Phillips, and Terlevich) diagram use optical emission-line ratios to separate galaxies. However, with growing availability of large surveys and high-resolution instruments, manually identifying galaxy types has become increasingly challenging. In this study, we investigate machine learning to classify active and star-forming galaxies using properties like stellar mass, stellar velocity dispersion, colour, redshift, and [O III] luminosity. These new approaches enable faster AGN/star-forming galaxy classification than the BPT diagram and provide a flexible, scalable alternative that can complement traditional diagnostics, particularly for large surveys or low-quality data. We employ four classification algorithms -- Decision Tree, Random Forest, Support Vector Classifier (SVC), and k-Nearest Neighbours (KNN) -- using the Galaxy Zoo 1 dataset derived from the SDSS sample. The dataset contains 47,675 galaxies within the redshift range 0.02--0.05, including 17,002 pure star-forming and 2,254 active galaxies, labeled using the BPT diagram. These labels train and evaluate our models through confusion matrices, learning curves, and receiver operating characteristic (ROC) curves. Among the four algorithms, the SVC and Random Forest models achieve the highest accuracy of approximately 93\%, while KNN shows the lowest at 88\%. Furthermore, we estimate supermassive black hole masses using stellar velocity dispersion ($σ$) and the $M_{\rm BH}-σ$ relation. We apply four regression models -- Random Forest Regressor, Support Vector Regressor (SVR), KNN Regressor, and Polynomial Regression. All four models produce similar results, with $R^2$ values from 0.75 to 0.77, indicating consistent performance.

astro-ph.GA↗

arXiv2025

Machine Learning for Exoplanet Detection: A Comparative Analysis Using Kepler Data

The 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.

astro-ph.EP↗

arXiv2025

Hydrodynamic simulations of the Disc of Gas Around Supermassive black holes (HDGAS) -II; The transition from neutral atomic to molecular gas phases

We use HDGAS hydrodynamic simulations to study the impact of active galactic nucleus (AGN) feedback on the conversion of atomic-gas to molecular-gas within the circumnuclear-disc (CND) of a typical AGN-dominated galaxy. The comparison of CI, CII, and CO line intensities and their ratios in the HDGAS post-processing radiative-transfer analysis reveals the complex interplay between AGN-activity, cold molecular gas properties, and the physical processes governing the evolution of star-formation in galaxies. Our results demonstrate that the CI/CO intensity ratio serves as a reliable indicator of the atomic-to-molecular gas transition. We present the probability distribution function (PDF) and abundance trends of various metal species related to molecular H$2$ gas, highlighting differences in clumpiness and intensity maps between AGN feedback and NoAGN models. The profile of the integrated intensity (moment-0) maps shows that the AGN-feedback model exhibits a lower CI/CO intensity ratio in the vicinity of the supermassive black hole (< 50 pc), indicating a smaller atomic-gas abundance and the presence of positive AGN-feedback. Our simulations have successfully predicted the presence of faint-CO emissions extending to larger radii from the galactic center. We also explore the relationships between CII/CO and CI/CII intensity ratios, as well as the ratios versus CO intensity, which provides insights into the "CO-dark" issues. One notable feature in the later time-scale of the AGN model is the presence of a "CO-dark" region, where the intensity of CO emission ($\rm I_{CO}$) is depleted relative to the H$_2$ column density ($N_{\rm H_2}$) compared to the NoAGN model.

astro-ph.GA↗

arXiv2022

The First Photometric Study of AH Mic Contact Binary System

The first multi-color light curve analysis of the AH Mic binary system is presented. This system has very few past observations from the southern hemisphere. We extracted the minima times from the light curves based on the Markov Chain Monte Carlo (MCMC) approach and obtained a new ephemeris. To provide modern photometric light curve solutions, we used the Physics of Eclipsing Binaries (Phoebe) software package and the MCMC approach. Light curve solutions yielded a system temperature ratio of 0.950, and we assumed a cold star-spot for the hotter star based on the O'Connell effect. This analysis reveals that AH Mic is a W-subtype W UMa contact system with a fill-out factor of 21.3% and a mass ratio of 2.32. The absolute physical parameters of the components are estimated by using the Gaia Early Data Release 3 (EDR3) parallax method to be M_h(M_Sun)=0.702(26), M_c(M_Sun)=1.629(104), R_h(R_Sun)=0.852(21), R_c(R_Sun)=1.240(28), L_h(L_Sun)=0.618(3) and L_c(L_Sun)=1.067(7). The orbital angular momentum of the AH Mic binary system was found to be 51.866(35). The components' positions of this system are plotted in the Hertzsprung-Russell (H-R) diagram.

astro-ph.SR↗