arXiv subjects

Mahdiyar Mousavi-Sadr

Publications and source records attributed to Mahdiyar Mousavi-Sadr.

7 recordsLinked to original sources

arXiv2026

Unveiling the roles of thermal and nonthermal processes in the ISM & IGM structure formation and evolution of galaxies with SKAO

Investigating the thermal and nonthermal processes in the interstellar medium (ISM) and intergalactic medium (IGM) is vital to understanding the evolution of galaxies over cosmic time. Resolved observations with SKA pathfinders show that the nonthermal processes, in which magnetic fields and cosmic rays are involved, can decelerate the formation of massive stars in strongly magnetized regions in nearby galaxies. They can also contribute to the onset of winds and outflows in galaxies. The effects of these processes are stronger at higher redshifts as a result of star formation activities. The SKA Observatory will allow a major breakthrough by mapping the thermal and nonthermal processes in distant universe galaxies, shedding light on the role of the ISM and IGM in the evolution of galaxies. We demonstrate this by simulating the radio continuum and HI emission from local galaxies back to high redshifts. Our simulations show that the AA4 surveys will make it possible to trace the thermal and nonthermal processes of the ISM in galaxies that are analogs to M51 and NGC6946, traced in continuum beyond cosmic noon (z=2-3) and the gas content traced by HI beyond z=1. Both simulations and precursor observations indicate the importance of nonthermal feedback at cosmic noon.

astro-ph.GA↗

arXiv2026

Opening new parameter space windows on galaxy/AGN co-evolution with SKA radio continuum surveys

In this chapter we provide an overview of the science enabled by the SKAO, focusing on galaxy/AGN co-evolution studies. In particular we discuss a number of radio continuum `reference' surveys with the SKAO, highlighting the role they can play in advancing this research field with respect to the pre-SKAO era. Alongside well-explored scenarios for wedding cake-like, tiered extragalactic surveys at specific frequencies, we also address the scope for complementary efforts to obtain deep multi-frequency imaging over parts of (an) extragalactic field(s). In addition to providing key information on the physical properties of the emitting sources, such multi-frequency imaging will make important contributions to the calibration of observables from surveys with sparser radio spectral coverage. In this context, we explore possible pathways that can fully exploit the SKAO from initial (AA*) to baseline capabilities (AA4). Finally, we highlight observational synergies with other major facilities -- for wide field and targeted follow-up science -- that will be operational in the 2030s, and for which joint coverage of extragalactic fields will generate significant legacy value

astro-ph.GA↗

arXiv2025

Exoplanetary radio emission predictions and detectability in the SKA era

Radio observations provide a window into a planet's interior and play a crucial role in studying its atmosphere and surface, key factors to find potential habitability. The discovery of thousands of exoplanets, together with advances in radio astronomy through the Square Kilometre Array (SKA), motivates the search for planetary-scale radio emissions. Here, we employ the radiometric Bode's law (RBL) and machine learning techniques to analyze a dataset of 1330 confirmed exoplanets, aiming to estimate their potential radio emission. Permutation Importance (PI) and SHapley Additive exPlanations (SHAP) analyses indicate that a planet's mass, radius, orbital semi-major axis, and distance from Earth are sufficient to dependably forecast its radio flux and frequency. The random forest model accurately reproduces these radio characteristics, confirming its reliability for exoplanetary radio predictions. Considering observational constraints, we find that 64 exoplanets could generate signals detectable by the SKA, 52 of which remain observable in the intermediate AA* deployment. Among these, MASCARA-1 b stands out with a predicted flux of 7.209 mJy at 135.1 MHz, making it an excellent SKA-Low target. Meanwhile, WASP-18 b, with a flux of 18.638 mJy peaking at 812.9 MHz, is the most promising candidate for SKA-Mid. These results show that the SKA can detect gas giants, such as MASCARA-1 b (SNR>400) and WASP-18 b (SNR>4236), within feasible integration times. Additionally, we identify four candidates (HATS-18 b, WASP-12 b, WASP-103 b, and WASP-121 b) that are likely affected by radio quenching, highlighting the importance of considering this effect in target selection for observation campaigns.

astro-ph.EP↗

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↗

arXiv2024

Exoplanets Prediction in Multi-Planetary Systems and Determining the Correlation Between the Parameters of Planets and Host Stars Using Artificial Intelligence

The number of extrasolar planets discovered is increasing, so that more than five thousand exoplanets have been confirmed to date. Now we have an opportunity to test the validity of the laws governing planetary systems and take steps to discover the relationships between the physical parameters of planets and stars. Firstly, we present the results of a search for additional exoplanets in 229 multi-planetary systems that house at least three or more confirmed planets, employing a logarithmic spacing between planets in our Solar System known as the Titius-Bode (TB) relation. We find that the planets in $\sim53\%$ of these systems adhere to a logarithmic spacing relation remarkably better than the Solar System planets. We predict the presence of 426 additional exoplanets, 47 of which are located within the habitable zone (HZ), and five of the 47 planets have a maximum mass limit of 0.1-2$M_{\oplus}$ and a maximum radius lower than 1.25$R_{\oplus}$. Secondly, we employ efficient machine learning approaches to analyze a dataset comprising 762 confirmed exoplanets and eight Solar System planets, aiming to characterize their fundamental quantities. We classify the data into two main classes: 'small' and 'giant' planets, with cut-off values at $R_{p}=8.13R_{\oplus}$ and $M_{p}=52.48M_{\oplus}$. Giant planets have lower densities, suggesting higher H-He mass fractions, while small planets are denser, composed mainly of heavier elements. We highlight that planetary mass, orbital period, and stellar mass play crucial roles in predicting exoplanet radius. Notably, our study reveals a noteworthy result: for giant planets, we observe a strong correlation between planetary radius and the mass of their host stars, which might provide intriguing insights into the relationship between giant planet formation and stellar characteristics.

astro-ph.EP↗

arXiv2023

Revisiting mass-radius relationships for exoplanet populations: a machine learning insight

The growing number of exoplanet discoveries and advances in machine learning techniques have opened new avenues for exploring and understanding the characteristics of worlds beyond our Solar System. In this study, we employ efficient machine learning approaches to analyze a dataset comprising 762 confirmed exoplanets and eight Solar System planets, aiming to characterize their fundamental quantities. By applying different unsupervised clustering algorithms, we classify the data into two main classes: 'small' and 'giant' planets, with cut-off values at $R_{p}=8.13R_{\oplus}$ and $M_{p}=52.48M_{\oplus}$. This classification reveals an intriguing distinction: giant planets have lower densities, suggesting higher H-He mass fractions, while small planets are denser, composed mainly of heavier elements. We apply various regression models to uncover correlations between physical parameters and their predictive power for exoplanet radius. Our analysis highlights that planetary mass, orbital period, and stellar mass play crucial roles in predicting exoplanet radius. Among the models evaluated, the Support Vector Regression consistently outperforms others, demonstrating its promise for obtaining accurate planetary radius estimates. Furthermore, we derive parametric equations using the M5P and Markov Chain Monte Carlo methods. Notably, our study reveals a noteworthy result: small planets exhibit a positive linear mass-radius relation, aligning with previous findings. Conversely, for giant planets, we observe a strong correlation between planetary radius and the mass of their host stars, which might provide intriguing insights into the relationship between giant planet formation and stellar characteristics.

astro-ph.EP↗

arXiv2021

Exoplanets Prediction in Multiplanetary Systems

We present the results of a search for additional exoplanets in all multiplanetary systems discovered to date, employing a logarithmic spacing between planets in our Solar System known as the Titius-Bode (TB) relation. We use the Markov Chain Monte Carlo method and separately analyse 229 multiplanetary systems that house at least three or more confirmed planets. We find that the planets in $\sim53\%$ of these systems adhere to a logarithmic spacing relation remarkably better than the Solar System planets. Using the TB relation, we predict the presence of 426 additional exoplanets in 229 multiplanetary systems, of which 197 candidates are discovered by interpolation and 229 by extrapolation. Altogether, 47 predicted planets are located within the habitable zone (HZ) of their host stars, and five of the 47 planets have a maximum mass limit of 0.1-2$M_{\oplus}$ and a maximum radius lower than 1.25$R_{\oplus}$. Our results and prediction of additional planets agree with previous studies' predictions; however, we improve the uncertainties in the orbital period measurement for the predicted planets significantly.

astro-ph.EP↗