arXiv subjects

Mahdi Abdollahi

Publications and source records attributed to Mahdi Abdollahi.

12 recordsLinked to original sources

arXiv2026

Systematic and Statistical Uncertainties in Cepheid PL Relations: Incorporating a Cross-Filter Random-Phase Mitigation Approach

The Period-Luminosity (PL) relation of Cepheid variable stars is a fundamental tool for measuring extragalactic distances and constraining the Hubble constant (H0). Achieving high precision in PL based distances requires careful consideration of both systematic and statistical uncertainties. We review the main sources of these uncertainties in PL relations, highlighting the increasing impact of random-phase errors in single-epoch observations from limited temporal coverage, such as those obtained with the James Webb Space Telescope (JWST). We discuss mitigation strategies for systematic errors, including photo metric calibration offsets, metallicity effects, blending, and parallax biases, and quantify key contributors to statistical errors, such as photometric noise, intrinsic scatter, and phase-sampling limitations. Special attention is given to a recently proposed cross-filter random-phase correction method (Abdollahi et al., 2025), which recovers mean magnitudes from single-epoch data by exploiting correlations between PL residuals in different bands. This technique reduces the dispersion in the infrared PL relation by 28%, equivalent to an order-of-magnitude increase in effective temporal sampling, demonstrating an efficient path to improving Cepheid-based distance measurements and the precision of H0.

astro-ph.SR↗

arXiv2026

The Isaac Newton Telescope Monitoring Survey of Local Group Dwarf Galaxies-VIII. A Census of Long-Period Variable Stars across the Andromeda Dwarf Satellite System

We present a comprehensive catalog, in the Sloan $i$ and Harris $V$ filters, of long-period variable (LPV) stars in the spheroidal dwarf satellites of the Andromeda galaxy, based on a dedicated survey for variable stars in Local Group dwarf systems. Using photometric time-series data obtained with the Wide Field Camera (WFC) on the 2.5 m Isaac Newton Telescope (INT), we identify approximately 2800 LPV candidates across 17 Andromeda satellites, spanning a broad range in luminosity and variability amplitude. This study is accompanied by a public data release that includes two comprehensive catalogs, a catalog of the complete stellar populations for each galaxy and a separate catalog listing all identified LPV candidates. Both are available through CDS/VizieR and provide a valuable resource for investigating quenching timescales, stellar mass distributions, and the effects of mass-loss and dust production in dwarf galaxies. We derive updated structural parameters, including newly measured half-light radii, and determine distance moduli using the Tip of the Red Giant Branch (TRGB) method with Sobel-filter edge detection, yielding values between $23.38\pm0.06$ and $25.35\pm0.06$ mag.

astro-ph.GA↗

arXiv2025

Metallicity Effects on Machine Learning Classification of Dusty Stellar Sources in the Magellanic Clouds

Differences in metallicity between the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC) offer an opportunity to examine whether environmental metallicity affects the performance of machine learning models in classifying dusty stellar sources. The five stellar classes studied include young stellar objects (YSOs), red supergiants (RSGs), post-asymptotic giant branch stars (PAGBs), and oxygen- and carbon-rich asymptotic giant branch stars (OAGBs and CAGBs), which are key phases of stellar evolution involved in dust production. Using spectroscopically labeled data from the Surveying the Agents of Galaxy Evolution (SAGE) project, we trained and evaluated a probabilistic random forest (PRF) classifier with four approaches: (1) separate training on LMC and SMC, including all five classes, (2) excluding the underpopulated PAGB class, (3) combined LMC and SMC datasets, and (4) cross-galaxy training and testing. The model achieved 93\% accuracy on the SMC and 88\% on the LMC across all five classes. In the SMC, PAGB sources were misclassified as YSOs, mainly because of their small sample size (4 objects). When PAGB was excluded, both the LMC and the SMC reached 92\% accuracy. A combined dataset produced the same accuracy, and cross-galaxy training yielded similar results, indicating that metallicity does not significantly impact model performance. A comparison of absolute CMDs for the LMC and SMC confirms their similarity in stellar populations. These findings suggest that environmental metallicity has little effect on ML-based classification of dusty stellar sources, supporting the use of combined datasets and cross-galaxy models in low-metallicity environments.

astro-ph.GA↗

arXiv2025

Mitigating Random-Phase Sampling Noise in the Cepheid Period-Luminosity Relation: A Cross-Filter Consistency Approach

The Period-Luminosity (PL) relation is usually derived using time-averaged magnitudes, which require multiple-epoch observations to determine periods and adequately sample the light curves. Although single-epoch observations are more practical and require significantly less observational effort, they inherently introduce greater photometric scatter, leading to an increased dispersion in the derived Period-Luminosity relations. In this paper, we explore, in detail, a method that transforms single random-phase data to their mean-light values, using information obtained in other bands for the same Cepheid. This approach enables the accurate re-construction of mean-light PL relations for wavelengths observed with space-based facilities, for instance, where the number of epochs per star makes simple averaging or template fitting less than optimal, with the latter requiring very high-precision periods for predictive phasing. While applying this technique across multiple bands, from optical to mid-IR, we focus particularly on widely separated bands covering the mid-IR to the optical. We showcase this method using the J band (as being observed by JWST) as the random-phase component. Our results show that this correction reduces the scatter of the PL relation in the J band by a factor of approximately $0.7\times$, equivalent to increasing the number of random-phase observations by a factor of 10, needed to obtain the same increase in precision as delivered here.

astro-ph.IM↗

arXiv2025

Comparison of Photometric and Spectroscopic Labels in Classifying Dusty Stellar Sources Using Machine Learning in the Magellanic Clouds

Dusty stellar sources, including young stellar objects (YSOs) and evolved stars such as oxygen- and carbon-rich AGBs (OAGBs, CAGBs), red supergiants (RSGs), and post-AGB stars (PAGBs), play a key role in the chemical enrichment of galaxies. Photometric surveys in the Magellanic Clouds have cataloged many such objects, but their classifications are often uncertain due to overlaps between populations. We trained machine learning models on spectroscopically labeled data from the SAGE project and applied them to photometric catalogs. The spectroscopic model achieves about 89\% accuracy. Applied to photometric labels, nearly all OAGBs are correctly identified, and YSOs have a 95\% confirmation rate. In contrast, 16\% of CAGBs are reclassified as OAGBs, only 8\% of RSGs retain their labels, and fewer than half of PAGBs are confirmed. Photometry is thus reliable for abundant populations with distinct signatures, but spectroscopic confirmation remains essential for rare or overlapping stellar classes.

astro-ph.GA↗

arXiv2025

Dusty stellar sources classification by implementing machine learning methods based on spectroscopic observations in the Magellanic Clouds

Dusty stellar point sources are a significant stage in stellar evolution and contribute to the metal enrichment of galaxies. These objects can be classified using photometric and spectroscopic observations with color-magnitude diagrams (CMD) and infrared excesses in spectral energy distributions (SED). We employed supervised machine learning spectral classification to categorize dusty stellar sources, including young stellar objects (YSOs) and evolved stars (oxygen- and carbon-rich asymptotic giant branch stars, AGBs), red supergiants (RSGs), and post-AGB (PAGB) stars in the Large and Small Magellanic Clouds, based on spectroscopic labeled data from the Surveying the Agents of Galaxy Evolution (SAGE) project, which used 12 multiwavelength filters and 618 stellar objects. Despite missing values and uncertainties in the SAGE spectral datasets, we achieved accurate classifications. To address small and imbalanced spectral catalogs, we used the Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic data points. Among models applied before and after data augmentation, the Probabilistic Random Forest (PRF), a tuned Random Forest (RF), achieved the highest total accuracy, reaching $\mathbf{89\%}$ based on recall in categorizing dusty stellar sources. Using SMOTE does not improve the best model's accuracy for the CAGB, PAGB, and RSG classes; it remains $\mathbf{100\%}$, $\mathbf{100\%}$, and $\mathbf{88\%}$, respectively, but shows variations for OAGB and YSO classes. We also collected photometric labeled data similar to the training dataset, classifying them using the top four PRF models with over $\mathbf{87\%}$ accuracy. Multiwavelength data from several studies were classified using a consensus model integrating four top models to present common labels as final predictions.

astro-ph.GA↗

arXiv2025

Machine Learning Classification of Young Stellar Objects and Evolved Stars in the Magellanic Clouds Using the Probabilistic Random Forest Classifier

The Magellanic Clouds (MCs) are excellent locations to study stellar dust emission and its contribution to galaxy evolution. Through spectral and photometric classification, MCs can serve as a unique environment for studying stellar evolution and galaxies enriched by dusty stellar point sources. We applied machine learning classifiers to spectroscopically labeled data from the Surveying the Agents of Galaxy Evolution (SAGE) project, which involved 12 multiwavelength filters and 618 stellar objects at the MCs. We classified stars into five categories: young stellar objects (YSOs), carbon-rich asymptotic giant branch (CAGB) stars, oxygen-rich AGB (OAGB) stars, red supergiants (RSG), and post-AGB (PAGB) stars. Following this, we augmented the distribution of imbalanced classes using the Synthetic Minority Oversampling Technique (SMOTE). Therefore, the Probabilistic Random Forest (PRF) classifier achieved the highest overall accuracy, reaching ${89\%}$ based on the recall metric, in categorizing dusty stellar sources before and after data augmentation. In this study, SMOTE did not impact the classification accuracy for the CAGB, PAGB, and RSG categories but led to changes in the performance of the OAGB and YSO classes.

astro-ph.GA↗

arXiv2025

Star Formation History of the Local Group Dwarf Irregular Galaxy, NGC 6822

NGC 6822 is an isolated dwarf irregular galaxy in the local group at a distance of 490 kpc. In this paper, we present the star formation history (SFH) within a field with a radius of 3 kpc, beyond the optical body of the galaxy (1.2 kpc). We utilized a novel method based on evolved asymptotic giant branch (AGB) stars. We collected the Near infrared data of 329 variable stars, including long-period and amplitude variables and Carbon-rich AGB stars. We used stellar evolutionary track and theoretical isochrones to obtain the birth mass, age, and pulsation duration of the detected stars to calculate the star formation rate (SFR) and trace the SFH of the galaxy. We studied the star formation history of the galaxy for the mean metallicity value (Z) of 0.003. We reconstructed the SFH for two regions. The bar region, a central rectangular area, and the outer region, which covers a circular field beyond the bar region and extends to a radius of 3 kpc. Our results show a significant burst of star formation around 2.6 and 2.9 Gyr ago in the bar and outer regions, respectively. Additionally, we observed a notable enhancement in the SFR in the bar region over the past 200 Myr.

astro-ph.GA↗

arXiv2024

Deciphering Galactic Halos: A Detailed Review of Star Formation in NGC 5128 (Cen A)

NGC 5128 (Centaurus A), the closest giant elliptical galaxy outside the Local Group to the Milky Way, is one of the brightest extragalactic radio sources. It is distinguished by a prominent dust lane and powerful jets, driven by a supermassive black hole at its core. Using previously identified long-period variable (LPV) stars from the literature, this study aims to reconstruct the star formation history (SFH) of two distinct regions in the halo of NGC 5128. These regions reveal remarkably similar SFHs, despite being located about 28 kpc apart on opposite sides of the galaxy's center. In Field 1, star formation rates (SFRs) show notable increases at approximately 800 Myr and 3.8 Gyr ago. Field 2 exhibits similar peaks at these times, along with an additional rise around 6.3 Gyr ago. The increase in SFR around 800 Myr ago is consistent with earlier research suggesting a merger event. Since no LPV catalog exists for the central region of NGC 5128, we focused our investigation on its outer regions, which has provided new insights into the complex evolutionary history of this cornerstone galaxy. The SFH traced by LPVs supports a scenario in which multiple events of nuclear activity have triggered episodic, jet-induced star formation.

astro-ph.GA↗

arXiv2024

Detection of the Long Period Variable Stars of And II Dwarf Satellite galaxy

We conducted an extensive study of the spheroidal dwarf satellite galaxies around the Andromeda galaxy to produce an extensive catalog of LPV stars. The optical monitoring project consists of 55 dwarf galaxies and four globular clusters that are members of the Local Group. We have made observations of these galaxies using the WFC mounted on the 2.5 m INT in nine different periods, both in the i-band filter Sloan and in the filter V-band Harris. We aim to select AGB stars with brightness variations larger than 0.2 mag to investigate the evolutionary processes in these dwarf galaxies. The resulting catalog of LPV stars in Andromeda's satellite galaxies offers updated information on features like half-light radii, TRGB magnitudes, and distance moduli. This manuscript will review the results obtained for And II galaxy. Using the Sobel filter, we have calculated the distance modulus for this satellite galaxy, which ranges from 23.90 to 24.11 mag.

astro-ph.GA↗

arXiv2024

The Complex Star Formation History of the Halo of NGC 5128 (Cen A)

NGC 5128 (Cen A) is the nearest giant elliptical galaxy and one of the brightest extragalactic radio sources in the sky, boasting a prominent dust lane and jets emanating from its nuclear supermassive black hole. In this paper, we construct the star formation history (SFH) of two small fields in the halo of NGC 5128: a northeastern field (Field 1) at a projected distance of $\sim 18.8$ kpc from the center, and a southern field (Field 2) $\sim 9.9$ kpc from the center. Our method is based on identifying long period variable (LPV) stars that trace their sibling stellar population and hence historical star formation due to their high luminosity and strong variability; we identified 395 LPVs in Field 1 and 671 LPVs in Field 2. Even though the two fields are $\sim 28$ kpc apart on opposite sides from the center, they show similar SFHs. In Field 1, star formation rates (SFRs) increased significantly around $t\sim 800$ Myr and $t\sim 3.8$ Gyr; and in Field 2, SFRs increased considerably around $t\sim 800$ Myr, $t\sim 3.8$ Gyr, and $t\sim 6.3$ Gyr, where $t$ is look--back time. The increase in SFR $\sim 800$ Myr ago agrees with previous suggestions that the galaxy experienced a merger around that time. The SFH reconstructed from LPVs supports a scenario in which multiple episodes of nuclear activity lead to episodic jet-induced star formation. While there is no catalog of LPVs for the central part of NGC 5128, applying our method to the outer regions (for the first time in a galaxy outside the Local Group) has enabled us to put constraints on the complex evolution of this cornerstone galaxy.

astro-ph.GA↗

arXiv2023

Hierarchical Classification of Variable Stars Using Deep Convolutional Neural Networks

The importance of using fast and automatic methods to classify variable stars for large amounts of data is undeniable. There have been many attempts to classify variable stars by traditional algorithms like Random Forest. In recent years, neural networks as classifiers have come to notice because of their lower computational cost compared to traditional algorithms. This paper uses the Hierarchical Classification technique, which contains two main steps of predicting class and then subclass of stars. All the models in both steps have same network structure and we test both Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Our pre-processing method uses light curves and period of stars as input data. We consider most of the classes and subclasses of variable stars in OGLE-IV database and show that using Hierarchical Classification technique and designing appropriate preprocessing can increase accuracy of predicting smaller classes, ACep and T2Cep. We obtain an accuracy of 98% for class classification and 93% for subclasses classification.

astro-ph.SR↗