arXiv subjects

Mohammad Noormohammadi

Publications and source records attributed to Mohammad Noormohammadi.

3 recordsLinked to original sources

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↗

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↗

arXiv2023

The membership of stars, density profile and mass segregation in open clusters using a new machine learning-based method

A combination of two unsupervised machine learning algorithms, DBSCAN and GMM are used to find members with a high probability of twelve open clusters, M38, NGC2099, Coma Ber, NGC752, M67, NGC2243, Alessi01, Bochum04, M34, M35, M41, and M48, based on Gaia DR3. These clusters have different ages, distances, and numbers of members which makes a suitable cover of these parameters situation to analyze this method. We have identified 752, 1725, 116, 269, 1422, 936, 43, 38, 743, 1114, 783, and 452, probable and possible members with a higher probability than 0.8 for M38, NGC2099, Coma Ber, NGC752, M67, NGC2243, Alessi01, Bochum04, M34, M35, M41, and M48, respectively. Moreover, we obtained the tidal radius, core radius, and clear evidence of mass segregation in ten clusters. From an examination of the high-quality color-magnitude data of the cluster, we obtained one white dwarf for each of NGC752, Coma Ber and M67. In the young open cluster M38, we found all members inside the tidal radius however in the older clusters we found some members outside of the tidal radius, indicating that the young open clusters had not enough time to form clear tidal tails. It is seen that mass segregation occurs at a higher rate in older clusters than the younger ones.

astro-ph.GA↗