arXiv subjects

Md Mahmudunnobe

Publications and source records attributed to Md Mahmudunnobe.

5 recordsLinked to original sources

arXiv2026

AGN-DB: A Unified Multi-Wavelength Database of Active Galactic Nuclei

We present the Active Galactic Nuclei Database (AGN-DB), a comprehensive, multi-wavelength catalog compiled from more than 100 publicly available AGN catalogs and samples released by the end of 2025, spanning radio to $\gamma$-ray wavelengths. The database contains approximately 8.1 million unique sources, approximately 7.8 million of which remain after flagging stellar contaminants, and approximately 6.8 million of these are classified as AGN. Source cross-matching across catalogs is performed using Lyra, a Bayesian likelihood-ratio framework that jointly considers positional uncertainties, source densities, and photometric information to compute posterior match probabilities. The resulting catalog provides astrometric coordinates, redshifts, photometry, and classifications for each unique source. All multi-catalog provenance is preserved. For every property, we store the full array of values and originating catalog identifiers, enabling multi-epoch and multi-survey analyses. In this paper, we describe the AGN-DB pipeline, including the cross-matching methodology, and present the statistical properties of the v1.0 catalog. AGN-DB is designed to enable population studies, spectral energy distribution modeling, AGN classification, and variability analyses at an unprecedented scale. Its pipeline is designed to facilitate the integration of new catalogs, allowing AGN-DB to be updated regularly, with releases planned at least annually.

astro-ph.GA↗

arXiv2026

Obscured AGN at z < 1.5: X-ray to Far-Infrared SEDs and Host Galaxy Morphologies in the GOODS Fields

We present an analysis of spectral energy distributions (SEDs), galaxy light profiles, and visual morphological classifications for 194 X-ray luminous AGN (intrinsic absorption-corrected log10 LX(0.5 to 7 keV) less than 42.5, with a maximum of 45.2 ergs per second) at redshift z less than 1.5 in the GOODS fields. We generate X-ray to far-infrared SEDs normalized at 1 micron for all AGN and sort them according to their emission slopes in the ultraviolet and infrared. We visually classify their host galaxy morphologies and compute their bulge-to-total light ratios using the software Galaxy Shapes of Light (galight). Most (94 percent) GOODS AGN exhibit obscured SEDs, defined by diminished ultraviolet and/or mid-infrared emission, while only 6 percent show unobscured, quasar-like SEDs. Secular processes appear to play a large role in stimulating AGN emission, as only around one-third of galaxies are undergoing interactions. We also describe the morphological identification of a population of suspected post-merger spheroid galaxies with obscured ultraviolet and infrared SEDs, and distinguish them from the host galaxies of AGN with less obscuration in the ultraviolet or infrared.

astro-ph.GA↗

arXiv2024

Membership determination in open clusters using the DBSCAN Clustering Algorithm

In this paper, we apply the machine learning clustering algorithm Density Based Spatial Clustering of Applications with Noise (DBSCAN) to study the membership of stars in twelve open clusters (NGC~2264, NGC~2682, NGC~2244, NGC~3293, NGC~6913, NGC~7142, IC~1805, NGC~6231, NGC~2243, NGC 6451, NGC 6005 and NGC 6583) based on Gaia DR3 Data. This sample of clusters spans a variety of parameters like age, metallicity, distance, extinction and a wide parameter space in proper motions and parallaxes. We obtain reliable cluster members using DBSCAN as faint as $G \sim 20$ mag and also in the outer regions of clusters. With our revised membership list, we plot color-magnitude diagrams and we obtain cluster parameters for our sample using ASteCA and compare it with the catalog values. We also validate our membership sample by spectroscopic data from APOGEE and GALAH for the available data. This paper demonstrates the effectiveness of DBSCAN in membership determination of clusters.

astro-ph.GA↗

arXiv2024

Using GMM in Open Cluster Membership: An Insight

The unprecedented precision of Gaia has led to a paradigm shift in membership determination of open clusters where a variety of machine learning (ML) models can be employed. In this paper, we apply the unsupervised Gaussian Mixture Model (GMM) to a sample of thirteen clusters with varying ages ($log \ t \approx$ 6.38-9.64) and distances (441-5183 pc) from Gaia DR3 data to determine membership. We use ASteca to determine parameters for the clusters from our revised membership data. We define a quantifiable metric Modified Silhouette Score (MSS) to evaluate its performance. We study the dependence of MSS on age, distance, extinction, galactic latitude and longitude, and other parameters to find the particular cases when GMM seems to be more efficient than other methods. We compared GMM for nine clusters with varying ages but we did not find any significant differences between GMM performance for younger and older clusters. But we found a moderate correlation between GMM performance and the cluster distance, where GMM works better for closer clusters. We find that GMM does not work very well for clusters at distances larger than 3~kpc.

astro-ph.GA↗

arXiv2021

Membership of Stars in Open Clusters using Random Forest with Gaia Data

Membership of stars in open clusters is one of the most crucial parameters in studies of star clusters. Gaia opened a new window in the estimation of membership because of its unprecedented 6-D data. In the present study, we used published membership data of nine open star clusters as a training set to find new members from Gaia DR2 data using a supervised random forest model with a precision of around 90\%. The number of new members found is often double the published number. Membership probability of a larger sample of stars in clusters is a major benefit in determination of cluster parameters like distance, extinction and mass functions. We also found members in the outer regions of the cluster and found sub-structures in the clusters studied. The color magnitude diagrams are more populated and enriched by the addition of new members making their study more promising.

astro-ph.SR↗