arXiv subjects

Atefeh Javadi

Publications and source records attributed to Atefeh Javadi.

At least 19 recordsLinked to original sources

arXiv2026

The UKIRT M33 Monitoring Project: A 15-Year Near-Infrared Variable Star Catalogue for the Central Kiloparsec, and Prospects for Star Formation History and Dust Return

We present results from the UKIRT M33 Monitoring Project, a near-infrared survey of variable red giants in the Local Group spiral galaxy M33. Combining four independent photometric datasets spanning UIST (2003), UFTI (2005), WFCAM (2005-2007), and a new UKIRT Hemisphere Survey epoch (2018), we construct a homogenised 15.11-year $K$-band light-curve catalogue for 847 stars in the central kiloparsec, of which 771 (91 per cent) are variable. Cross-matching with archival Spitzer photometry identifies 120 dust-enshrouded AGB candidates. We compare the spatial coverage of this central catalogue with the earlier UIST-only (Paper I) and disc-wide WFCAM (Paper IV) variable-star surveys. Building on this catalogue, two forthcoming papers will (i) measure individual pulsation periods to reconstruct the star formation history of the central kiloparsec, and (ii) refine dust and gas mass-loss rates across the disc using the extended time baseline.

astro-ph.GA↗

arXiv2026

The UK Infrared Telescope M33 monitoring project. VI. Feedback from dusty stellar winds across the galactic disc

We have conducted a near-infrared monitoring campaign at the UK InfraRed Telescope (UKIRT), of the Local Group spiral galaxy M33 (Triangulum). In this sixth paper of the series, we measure the dust and gas mass-loss rates by the pulsating Asymptotic Giant Branch (AGB) stars and red supergiants (RSGs) across the stellar disc of M33. We combined our time-averaged near-IR photometry with the multi-epoch mid-IR photometry obtained with the Spitzer Space Telescope, and employed a combination of spectral energy distribution modelling and scaling relations. We found that the mass-loss rate is approximately proportional to luminosity (birth mass), with additional weaker dependence on pulsation period and/or amplitude (reflecting stellar evolution). As a population, AGB stars contribute most to the mass return into the interstellar medium (ISM). Super-AGB stars also reach very high mass-loss rates, in excess of $10^{-4}$ M$_\odot$ yr$^{-1}$. The mass loss of RSGs appears to be subject to different modes, with rates well below, around, and well above the nuclear burning timescale. The timescale for the dominant mass loss phase is $\sim0.6$-$2\times10^5$ yr, shorter than the thermal-pulsing AGB or RSG phases. The rate at which stars return mass to the ISM, $\sim0.1$ M$_\odot$ yr$^{-1}$ is about four times lower than the star formation rate, which would deplete the current ISM mass within about a Gyr, thus requiring additional, external gas supplies to sustain the long-term future of star formation in M33.

astro-ph.GA↗

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 unique capabilities of HST for stellar physics: Probing Atmospheric Structure, Chromospheres, and Mass Loss of Evolved Stars

Evolved stars are among the primary sources of chemical enrichment and dust production in galaxies. During the giant phases, stars return a substantial fraction of their mass to the interstellar medium (ISM) through stellar winds, enriching galaxies with newly synthesized elements and dust. However, the atmospheric structure and physical processes that initiate mass loss remain poorly constrained observationally. Understanding the origin, structure, and evolution of stellar chromospheres remains a long-standing problem in stellar astrophysics. While the mechanisms responsible for chromospheric heating and atmospheric dynamics are not fully understood even in the Sun, they become more complex in evolved stars due to pulsation, shocks, convection, extended atmospheres, and possible magnetic activity. Determining the thermal, density, and velocity structure of these extended atmospheres is therefore essential for understanding atmospheric heating, the onset of mass loss, and the late stages of stellar evolution. High-resolution NUV and FUV spectroscopy (R ~ 30,000-100,000) provided by HST/STIS occupies a unique observational parameter space that cannot be replaced by existing facilities. HST/STIS therefore remains essential for understanding the atmospheric physics and mass-loss processes of evolved stars. We highlight the need to preserve and prioritize high-resolution NUV and FUV spectroscopic capabilities with HST. Such programs would provide essential benchmarks for stellar atmosphere modeling, complement ongoing ALMA and optical observations, and help define future UV-optical capabilities for the Habitable Worlds Observatory (HWO).

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

Detection of Quadruple Structure Near the ASCC 32 Region via Machine Learning Methods

Multiple structures within stellar groups are an intriguing subject for theoretical and observational studies of stellar formation. With the accuracy and completeness of data from Gaia Data Release 3, we now have new opportunities to detect reliable members of stellar groups across a larger field of view than in previous studies. In this work, using machine learning methods and high-accuracy data, we investigate the possibility of detecting multiple structures within 500 arcmin of ASCC 32. We first applied DBSCAN to proper motion and parallax, as multiple structures tend to share similar values for these parameters. Next, we applied GMM to position, proper motion, and parallax for the members detected by DBSCAN. This approach allowed us to identify a filamentary structure among the DBSCAN-detected members. This structure contains all stellar groups previously identified in this region. Subsequently, based on the BIC score, we applied GMM to this filamentary structure. Since multiple structures exhibit distinct positional distributions, GMM was able to effectively separate all groups within the filament. Our methods successfully identified ASCC 32, OC 0395, and HSC 1865 within a 500 arcmin radius. Additionally, we found two distinct substructures within ASCC 32. These four groups exhibit a single main-sequence distribution in the CMD, with proper motion values within three times the standard deviation and slightly differing parallax values, despite having distinct spatial structures. Furthermore, these four groups share the same radial velocity distribution. We provide documentation demonstrating the formation of these stellar groups as a multiple structure, with improved membership identification compared to previous studies.

astro-ph.GA↗

arXiv2025

SEDust: A Pipeline for Deriving Best-Fit Spectral Energy Distributions from DUSTY Outputs

In this study, we introduce SEDust, a pipeline designed to identify the best-fitting spectral energy distributions from the outputs of the DUSTY code and compare them to observational data. The pipeline incorporates a grid of 24000 models, enabling robust fitting for both carbon- and oxygen-rich AGB stars. It calculates key physical parameters, including luminosity, optical depth, and mass-loss rate, and produces the corresponding best-fit SED plots. Using SEDust, we derived the specific mass-return rates for the galaxies NGC 147 and NGC 185. The specific mass-return rate of AGB stars in NGC 147 is $8.13 \times 10^{-12} \mathrm{yr}^{-1}$, while in NGC 185 it is $6.52 \times 10^{-11} \mathrm{yr}^{-1}$. These results indicate that the mass loss from evolved stars alone cannot account for the total mass budget required to sustain these galaxies, highlighting the need for additional sources or mechanisms of mass replenishment to resolve the observed discrepancies.

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

Long-period variable stars in NGC 147 and NGC 185-II. Their dust production

This study presents a comparative analysis of mass-loss and dust-production rates in the dwarf galaxies NGC 147 and NGC 185, focusing on long-period variables (LPVs) and pulsating asymptotic giant branch (AGB) stars as primary indicators of dust feedback into the interstellar medium. For NGC 147, the total mass-loss rate is calculated as $(9.44 \pm 3.78) \times 10^{-4} M_{sun} yr^{-1}$, with LPV luminosities ranging from $(6.20 \pm 0.25) \times 10^{2} L_\odot$ to $( 7.87 \pm 0.32) \times 10^{3} L_\odot $. In NGC 185, the total mass-loss rate is higher, at $(1.58 \pm 0.63) \times 10^{-3} M_{sun} yr^{-1}$, with LPV luminosities spanning $ (5.68 \pm 0.23) \times 10^{2} L_\odot $ to $(1.54 \pm 0.66) \times 10^{4} L_\odot$. A positive correlation is observed between stellar luminosity, intrinsic reddening due to circumstellar dust self-extinction, and elevated mass-loss rates. Additionally, comparisons of calculated dust injection rates, two-dimensional dust distribution maps, and observed dust masses provide evidence for a gravitational interaction between NGC 147 and the Andromeda galaxy, which influences the dust distribution within the system.

astro-ph.GA↗

arXiv2025

The Isaac Newton Telescope Monitoring Survey of Local Group Dwarf Galaxies. VII. Long-Period Variable Stars in the Nearest Starburst Dwarf Galaxy, IC 10

To identify long-period variable (LPV) stars in IC10 - the nearest starburst galaxy of the Local Group (LG) - we conducted an optical monitoring survey using the 2.5-m Isaac Newton Telescope (INT) with the wide-field camera (WFC) in the i-band and V-band from 2015 to 2017. We created a photometric catalog for 53,579 stars within the area of CCD4 of WFC ($\sim$ 0.07 deg$^2$ corresponding to 13.5 kpc$^2$ at the distance of IC10), of which we classified 536 and 380 stars as long-period variable candidates (LPVs), mostly asymptotic giant branch stars (AGBs) and red supergiants (RSGs), within CCD4 and two half-light radii of IC10, respectively. By comparing our output catalog to the catalogs from Pan-STARRS, Spitzer Space Telescope, Hubble Space Telescope (HST), and carbon stars from the Canada-France-Hawai'i Telescope (CFHT) survey, we determined the success of our detection method. We recovered $\sim$ 73% of Spitzer's sources in our catalog and demonstrated that our survey successfully identified 43% of the variable stars found with Spitzer, and also retrieved 40% of the extremely dusty AGB stars among the Spitzer variables. In addition, we successfully identified $\sim$ 70% of HST variables in our catalog. Furthermore, we found all the confirmed LPVs that Gaia DR3 detected in IC10 among our identified LPVs. This paper is the first in a series on IC10, presenting the variable star survey methodology and the photometric catalog, available to the public through the Centre de Données Astronomiques de Strasbourg.

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

Detection of Open Cluster Members Inside and Beyond Tidal Radius by Machine Learning Methods Based on Gaia DR3

In our previous work, we introduced a method that combines two unsupervised algorithms: DBSCAN and GMM. We applied this method to 12 open clusters based on Gaia EDR3 data, demonstrating its effectiveness in identifying reliable cluster members within the tidal radius. However, for studying cluster morphology, we need a method capable of detecting members both inside and outside the tidal radius. By incorporating a supervised algorithm into our approach, we successfully identified members beyond the tidal radius. In our current work, we initially applied DBSCAN and GMM to identify reliable members of cluster stars. Subsequently, we trained the Random Forest algorithm using DBSCAN and GMM-selected data. Leveraging the random forest, we can identify cluster members outside the tidal radius and observe cluster morphology across a wide field of view. Our method was then applied to 15 open clusters based on Gaia DR3, which exhibit a wide range of metallicity, distances, members, and ages. Additionally, we calculated the tidal radius for each of the 15 clusters using the King profile and detected stars both inside and outside this radius. Finally, we investigated mass segregation and luminosity distribution within the clusters. Overall, our approach significantly improved the estimation of the tidal radius and detection of mass segregation compared to previous work. We found that in Collinder 463, low-mass stars do not segregate in comparison to high-mass and middle-mass stars. Additionally, we detected a peak of luminosity in the clusters, some of which were located far from the center, beyond the tidal radius.

astro-ph.GA↗