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Register a new userUOCS. III. UVIT catalogue of open clusters with machine learning based membership using textit{Gaia} EDR3 astrometry
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We present a study of six open clusters (Berkeley 67, King 2, NGC 2420, NGC 2477, NGC 2682 and NGC 6940) using the Ultra Violet Imaging Telescope (UVIT) aboard textit{ASTROSAT} and textit{Gaia} EDR3. We used combinations of astrometric, photometric and systematic parameters to train and supervise a machine learning algorithm along with a Gaussian mixture model for the determination of cluster membership. This technique is robust, reproducible and versatile in various cluster environments. In this study, the textit{Gaia} EDR3 membership catalogues are provided along with classification of the stars as texttt{members, candidates} and texttt{field} in the six clusters. We could detect 200--2500 additional members using our method with respect to previous studies, which helped estimate mean space velocities, distances, number of members and core radii. UVIT photometric catalogues, which include blue stragglers, main-sequence and red giants are also provided. From UV--Optical colour-magnitude diagrams, we found that majority of the sources in NGC 2682 and a few in NGC 2420, NGC 2477 and NGC 6940 showed excess UV flux. NGC 2682 images have ten white dwarf detection in far-UV. The far-UV and near-UV images of the massive cluster NGC 2477 have 92 and 576 texttt{members} respectively, which will be useful to study the UV properties of stars in the extended turn-off and in various evolutionary stages from main-sequence to red clump. Future studies will carry out panchromatic and spectroscopic analysis of noteworthy members detected in this study.
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The membership determination for open clusters in noisy environments of the Milky Way is still an open problem. In this paper, our main aim is provide the membership probability of stars using proper motions and parallax values of stars using Gaia EDR3 astrometry. Apart from the Gaia astrometry, we have also used other photometric data sets like UKIDSS, WISE, APASS and Pan-STARRS1 in order to understand cluster properties from optical to mid-infrared regions. We selected 438 likely members with membership probability higher than $50%$ and G$le$20 mag. We obtained the mean value of proper motion as $mu_{x}=1.27pm0.001$ and $mu_{y}=-0.73pm0.002$ mas yr$^{-1}$. The clusters radius is determined as 7.5 arcmin (5.67 pc) using radial density profile. Our analysis suggests that NGC 1348 is located at a distance of $2.6pm0.05$ kpc. The mass function slope is found to be $1.30pm0.18$ in the mass range 1.0$-$4.1 $M_odot$, which is in fair agreement with Salpeters value within the 1$sigma$ uncertainty. The present study validates that NGC 1348 is a dynamically relaxed cluster. We computed the apex coordinates $(A, D)$ for NGC 1348 as $(A_circ, D_circ)$ = $(-23^{textrm{o}}.815pm 0^{textrm{o}}.135$, $-22^{textrm{o}}.228pm 0^{textrm{o}}.105)$. In addition, calculations of the velocity ellipsoid parameters (VEPs), matrix elements $mu_{ij}$, direction cosines ($l_j$, $m_j$, $n_j$) and the Galactic longitude of the vertex have been also conducted in this analysis.
We analyze the 3D morphology and kinematics of 13 open clusters (OCs) located within 500 pc of the Sun, using Gaia EDR3 and kinematic data from literature. Members of OCs are identified using the unsupervised machine learning method StarGO, using 5D parameters (X, Y, Z, $mu_alpha cosdelta, mu_delta$). The OC sample covers an age range of 25Myr--2.65Gyr. We correct the asymmetric distance distribution due to the parallax error using Bayesian inversion. The uncertainty in the corrected distance for a cluster at 500~pc is 3.0--6.3~pc, depending on the intrinsic spatial distribution of its members. We determine the 3D morphology of the OCs in our sample and fit the spatial distribution of stars within the tidal radius in each cluster with an ellipsoid model. The shapes of the OCs are well-described with oblate spheroids (NGC2547, NGC2516, NGC2451A, NGC2451B, NGC2232), prolate spheroids (IC2602, IC4665, NGC2422, Blanco1, Coma Berenices), or triaxial ellipsoids (IC2391, NGC6633, NGC6774). The semi-major axis of the fitted ellipsoid is parallel to the Galactic plane for most clusters. Elongated filament-like substructures are detected in three young clusters (NGC2232, NGC2547, NGC2451B), while tidal-tail-like substructures (tidal tails) are found in older clusters (NGC2516, NGC6633, NGC6774, Blanco1, Coma Berenices). Most clusters may be super-virial and expanding. $N$-body models of rapid gas expulsion with an SFE of $approx 1/3$ are consistent with clusters more massive than $250rm M_odot$, while clusters less massive than 250$rm M_odot$ tend to agree with adiabatic gas expulsion models. Only six OCs (NGC2422, NGC6633, and NGC6774, NGC2232, Blanco1, Coma Berenices) show clear signs of mass segregation.
We analysed the open clusters Czernik 2 and NGC 7654 using CCD UBV photometric and Gaia Early Data Release 3 (EDR3) photometric and astrometric data. Structural parameters of the two clusters were derived, including the physical sizes of Czernik 2 being r=5 and NGC 7654 as 8 min. We calculated membership probabilities of stars based on their proper motion components as released in the Gaia EDR3. To identify member stars of the clusters, we used these membership probabilities taking into account location and the impact of binarity on main-sequence stars. We used membership probabilities higher than $P=0.5$ to identify 28 member stars for Czernik 2 and 369 for NGC 7654. We estimated colour-excesses and metallicities separately using two-colour diagrams to derive homogeneously determined parameters. The derived $E(B-V)$ colour excess is 0.46(0.02) mag for Czernik 2 and 0.57(0.04) mag for NGC 7654. Metallicities were obtained for the first time for both clusters, -0.08(0.02) dex for Czernik 2 and -0.05(0.01) dex for NGC 7654. Keeping the reddening and metallicity as constant quantities, we fitted PARSEC models using colour-magnitude diagrams, resulting in estimated distance moduli and ages of the two clusters. We obtained the distance modulus for Czernik 2 as 12.80(0.07) mag and for NGC 7654 as 13.20(0.16) mag, which coincide with ages of 1.2(0.2) Gyr and 120(20) Myr, respectively. The distances to the clusters were calculated using the Gaia EDR3 trigonometric parallaxes and compared with the literature. We found good agreement between the distances obtained in this study and the literature. Present day mass function slopes for both clusters are comparable with the value of Salpeter (1955), being X=-1.37(0.24) for Czernik 2 and X=-1.39(0.19) for NGC 7654.
The main objective of this work is to determine the cluster members of 1876 open clusters, using positions and proper motions of the astrometric catalogue UCAC4. For this purpose we apply three different methods, all them based on a Bayesian approach, but with different formulations: a purely parametric method, another completely non-parametric algorithm, and a third, recently developed by Sampedro & Alfaro, using both formulations at different steps of the whole process. The first and second statistical moments of the members phase-space subspace, obtained after applying the three methods, are compared for every cluster. Although, on average, the three methods yield similar results, specific differences between them, as well as for some particular clusters, are also present. The comparison with other published catalogues shows good agreement. We have also estimated for the first time the mean proper motion for a sample of 18 clusters. The results are organized in a single catalogue formed by two main files, one with the most relevant information for each cluster, partially including that in UCAC4, and the other showing the individual membership probabilities for each star in the cluster area. The final catalogue, with an interface design that enables an easy interaction with the user, is available in electronic format at SSG-IAA (http://ssg.iaa.es/en/content/sampedro-cluster-catalog) website.
Open clusters are key targets for both Galaxy structure and evolution and stellar physics studies. Since textit{Gaia} DR2 publication, the discovery of undetected clusters has proven that our samples were not complete. Our aim is to exploit the Big Data capabilities of machine learning to detect new open clusters in textit{Gaia} DR2, and to complete the open cluster sample to enable further studies on the Galactic disc. We use a machine learning based methodology to systematically search in the Galactic disc, looking for overdensities in the astrometric space and identifying them as open clusters using photometric information. First, we use an unsupervised clustering algorithm, DBSCAN, to blindly search for these overdensities in textit{Gaia} DR2 $(l,b,varpi,mu_{alpha^*},mu_delta)$. After that, we use a deep learning artificial neural network trained on colour-magnitude diagrams to identify isochrone patterns in these overdensities, and to confirm them as open clusters. We find $582$ new open clusters distributed along the Galactic disc, in the region $|b| < 20$. We can detect substructure in complex regions, and identify the tidal tails of a disrupting cluster UBC~$274$ of $sim 3$ Gyr located at $sim 2$ kpc. Adapting the methodology into a Big Data environment allows us to target the search driven by physical properties of the open clusters, instead of being driven by its computational requirements. This blind search for open clusters in the Galactic disc increases in a $45%$ the number of known open clusters.
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