Hunting for open clusters in Gaia DR2: 582 new open clusters in the Galactic disc

Context. Open clusters are key targets for studies of Galaxy structure and evolution, and stellar physics. Since the Gaia data release 2 (DR2), the discovery of undetected clusters has shown that previous surveys were incomplete. Aims. Our aim is to exploit the Big Data capabilities of machine learning to detect new open clusters in Gaia DR2, and to complete the open cluster sample to enable further studies of the Galactic disc. Methods. We use a machine-learning based methodology to systematically search the Galactic disc for overdensities in the astrometric space and identify the open clusters using photometric information. First, we used an unsupervised clustering algorithm, DBSCAN, to blindly search for these overdensities in Gaia DR2 (l, b, ϖ, μα*, μδ), and then we used 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. Results. We find 582 new open clusters distributed along the Galactic disc in the region |b| < 20°. We detect substructure in complex regions, and identify the tidal tails of a disrupting cluster UBC 274 of ∼3 Gyr located at ∼2 kpc. Conclusions. Adapting the mentioned methodology to a Big Data environment allows us to target the search using the physical properties of open clusters instead of being driven by computational limitations. This blind search for open clusters in the Galactic disc increases the number of known open clusters by 45%.

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Extended halo of NGC 2682 (M 67) from Gaia DR2

Astronomy and Astrophysics ◽

10.1051/0004-6361/201935599 ◽

2019 ◽

Vol 627 ◽

pp. A119 ◽

Cited By ~ 6

Author(s):

R. Carrera ◽

M. Pasquato ◽

A. Vallenari ◽

L. Balaguer-Núñez ◽

T. Cantat-Gaudin ◽

...

Keyword(s):

Spatial Distribution ◽

Open Cluster ◽

Star Clusters ◽

Dynamical Evolution ◽

Open Clusters ◽

Galactic Disc ◽

The Past ◽

External Perturbations ◽

Galactic Potential ◽

Gaia Dr2

Context. NGC 2682 is a nearby open cluster that is approximately 3.5 Gyr old. Dynamically, most open clusters are expected to dissolve on shorter timescales of ≈1 Gyr. That it has survived until now means that NGC 2682 was likely much more massive in the past and is bound to have an interesting dynamical history. Aims. We investigate the spatial distribution of the stars in NGC 2682 to constrain dynamical evolution of the cluster. We particularly focus on the marginally bound stars in the cluster outskirts. Methods. We used Gaia DR2 data to identify NGC 2682 members up to a distance of ∼150 pc (10°). The two methods Clusterix and UPMASK were applied to this end. We estimated distances to obtain 3D stellar positions using a Bayesian approach to parallax inversion, with an appropriate prior for star clusters. We calculated the orbit of NGC 2682 using the GRAVPOT16 software. Results. The cluster extends up to 200′ (50 pc), which implies that its size is at least twice as large as previously believed. This exceeds the cluster Hill sphere based on the Galactic potential at the distance of NGC 2682. Conclusion. The extra-tidal stars in NGC 2682 may originate from external perturbations such as disc-shocking or dynamical evaporation from two-body relaxation. The former origin is plausible given the orbit of NGC 2682, which crossed the Galactic disc ≈40 Myr ago.

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Hunting for open clusters in Gaia DR2: the Galactic anticentre

Astronomy and Astrophysics ◽

10.1051/0004-6361/201935531 ◽

2019 ◽

Vol 627 ◽

pp. A35 ◽

Cited By ~ 15

Author(s):

A. Castro-Ginard ◽

C. Jordi ◽

X. Luri ◽

T. Cantat-Gaudin ◽

L. Balaguer-Núñez

Keyword(s):

Machine Learning ◽

Clustering Algorithm ◽

Open Clusters ◽

Fine Tuning ◽

Learning Methods ◽

Density Based Clustering ◽

Single Method ◽

Blind Search ◽

Physical Clusters ◽

Perseus Arm

Context. The Gaia Data Release 2 (DR2) provided an unprecedented volume of precise astrometric and excellent photometric data. In terms of data mining the Gaia catalogue, machine learning methods have shown to be a powerful tool, for instance in the search for unknown stellar structures. Particularly, supervised and unsupervised learning methods combined together significantly improves the detection rate of open clusters. Aims. We systematically scan Gaia DR2 in a region covering the Galactic anticentre and the Perseus arm (120° ≤ l ≤ 205° and −10° ≤ b ≤ 10°), with the goal of finding any open clusters that may exist in this region, and fine tuning a previously proposed methodology and successfully applied to TGAS data, adapting it to different density regions. Methods. Our methodology uses an unsupervised, density-based, clustering algorithm, DBSCAN, that identifies overdensities in the five-dimensional astrometric parameter space (l, b, ϖ, μα*, μδ) that may correspond to physical clusters. The overdensities are separated into physical clusters (open clusters) or random statistical clusters using an artificial neural network to recognise the isochrone pattern that open clusters show in a colour magnitude diagram. Results. The method is able to recover more than 75% of the open clusters confirmed in the search area. Moreover, we detected 53 open clusters unknown previous to Gaia DR2, which represents an increase of more than 22% with respect to the already catalogued clusters in this region. Conclusions. We find that the census of nearby open clusters is not complete. Different machine learning methodologies for a blind search of open clusters are complementary to each other; no single method is able to detect 100% of the existing groups. Our methodology has shown to be a reliable tool for the automatic detection of open clusters, designed to be applied to the full Gaia DR2 catalogue.

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ML-MOC: Machine Learning (kNN and GMM) based Membership determination for Open Clusters

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stab118 ◽

2021 ◽

Vol 502 (2) ◽

pp. 2582-2599

Author(s):

Manan Agarwal ◽

Khushboo K Rao ◽

Kaushar Vaidya ◽

Souradeep Bhattacharya

Keyword(s):

Machine Learning ◽

Open Cluster ◽

A Priori ◽

Gaussian Mixture ◽

Open Clusters ◽

Proper Motions ◽

Cluster Membership ◽

Priori Information ◽

Gaia Dr2 ◽

Good Agreement

ABSTRACT The existing open-cluster membership determination algorithms are either prior dependent on some known parameters of clusters or are not automatable to large samples of clusters. In this paper, we present ml-moc, a new machine-learning-based approach to identify likely members of open clusters using the Gaia DR2 data and no a priori information about cluster parameters. We use the k-nearest neighbour (kNN) algorithm and the Gaussian mixture model (GMM) on high-precision proper motions and parallax measurements from the Gaia DR2 data to determine the membership probabilities of individual sources down to G ∼ 20 mag. To validate the developed method, we apply it to 15 open clusters: M67, NGC 2099, NGC 2141, NGC 2243, NGC 2539, NGC 6253, NGC 6405, NGC 6791, NGC 7044, NGC 7142, NGC 752, Blanco 1, Berkeley 18, IC 4651, and Hyades. These clusters differ in terms of their ages, distances, metallicities, and extinctions and cover a wide parameter space in proper motions and parallaxes with respect to the field population. The extracted members produce clean colour–magnitude diagrams and our astrometric parameters of the clusters are in good agreement with the values derived in previous work. The estimated degree of contamination in the extracted members ranges between 2 ${{\ \rm per\ cent}}$ and 12 ${{\ \rm per\ cent}}$. The results show that ml-moc is a reliable approach to segregate open-cluster members from field stars.

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A Gaia DR2 view of the open cluster population in the Milky Way

Astronomy and Astrophysics ◽

10.1051/0004-6361/201833476 ◽

2018 ◽

Vol 618 ◽

pp. A93 ◽

Cited By ~ 180

Author(s):

T. Cantat-Gaudin ◽

C. Jordi ◽

A. Vallenari ◽

A. Bragaglia ◽

L. Balaguer-Núñez ◽

...

Keyword(s):

Stellar Evolution ◽

Milky Way ◽

Galactic Plane ◽

Galactic Disk ◽

Open Cluster ◽

Open Clusters ◽

Number Of Clusters ◽

History Of ◽

Gaia Dr2

Context. Open clusters are convenient probes of the structure and history of the Galactic disk. They are also fundamental to stellar evolution studies. The second Gaia data release contains precise astrometry at the submilliarcsecond level and homogeneous photometry at the mmag level, that can be used to characterise a large number of clusters over the entire sky. Aims. In this study we aim to establish a list of members and derive mean parameters, in particular distances, for as many clusters as possible, making use of Gaia data alone. Methods. We compiled a list of thousands of known or putative clusters from the literature. We then applied an unsupervised membership assignment code, UPMASK, to the Gaia DR2 data contained within the fields of those clusters. Results. We obtained a list of members and cluster parameters for 1229 clusters. As expected, the youngest clusters are seen to be tightly distributed near the Galactic plane and to trace the spiral arms of the Milky Way, while older objects are more uniformly distributed, deviate further from the plane, and tend to be located at larger Galactocentric distances. Thanks to the quality of Gaia DR2 astrometry, the fully homogeneous parameters derived in this study are the most precise to date. Furthermore, we report on the serendipitous discovery of 60 new open clusters in the fields analysed during this study.

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Performance analysis of machine learning techniques for the prediction of breast cancer in big data environment

10.1063/5.0011116 ◽

2020 ◽

Author(s):

Premalatha Jayapaul ◽

Aswini Balasundaram ◽

Kavi Priya Dharshini Seturamalingam ◽

Kavithra Sekar

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Big Data ◽

Performance Analysis ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Data Environment

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Machine Learning Research in Big Data Environment

2018 5th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2018) ◽

10.25236/iceeecs.2018.048 ◽

2018 ◽

Keyword(s):

Machine Learning ◽

Big Data ◽

Learning Research ◽

Data Environment

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The product quality risk assessment of e-commerce by machine learning algorithm on spark in big data environment

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-179305 ◽

2019 ◽

Vol 37 (4) ◽

pp. 4705-4715

Author(s):

Yi Liu ◽

Jiahuan Lu ◽

Feng Mao ◽

Kaidi Tong

Keyword(s):

Machine Learning ◽

Risk Assessment ◽

Big Data ◽

Product Quality ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Data Environment ◽

Quality Risk

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Investigation of the nearby open clusters with Gaia DR2 data

Proceedings of the International Astronomical Union ◽

10.1017/s1743921319006653 ◽

2019 ◽

Vol 14 (S351) ◽

pp. 502-506

Author(s):

Anton F. Seleznev ◽

Vladimir M. Danilov ◽

Giovanni Carraro

Keyword(s):

Three Dimensional ◽

Open Clusters ◽

Dimensional Structure ◽

Proper Motions ◽

Membership Probability ◽

First Results ◽

Alpha Persei ◽

Mass Functions ◽

Tidal Tails ◽

Gaia Dr2

AbstractGaia DR2 catalog provides a unique possibility to study the three-dimensional structure and the three-dimensional velocity field of the nearby open clusters. We can either select stars with a maximum membership probability and the most accurate values for the proper motions, parallaxes, and the radial velocities, or study these clusters statistically using overwhelmingly large areas of sky of tens by tens degrees. The second approach allows us to reveal the extensive outer parts of the clusters - a corona and the tidal tails and to study the luminosity and mass functions of these clusters. We present the first results of the investigation of several nearby open clusters, including Pleiades, Alpha Persei, Ruprecht 147.

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Open clusters as tracers on radial migration of the galactic disc

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa1079 ◽

2020 ◽

Vol 495 (3) ◽

pp. 2673-2681 ◽

Cited By ~ 1

Author(s):

Y Q Chen ◽

G Zhao

Keyword(s):

Open Clusters ◽

Important Process ◽

Galactic Disc ◽

Migration Distance ◽

Radial Migration ◽

Orbital Parameters ◽

Migration Rates ◽

Formation History ◽

Gaia Dr2

ABSTRACT Radial migration is an important process in the Galactic disc. A few open clusters show some evidence on this mechanism but there is no systematic study. In this work, we investigate the role of radial migration on the Galactic disc based on a large sample of 146 open clusters with homogeneous metallicity and age from Netopil et al. and kinematics calculated from Gaia DR2. The birth site Rb, guiding radius Rg, and other orbital parameters are calculated, and the migration distance |Rg − Rb| is obtained, which is a combination of metallicity, kinematics, and age information. It is found that 44 per cent open clusters have |Rg − Rb| < 1 kpc, for which radial migration (churning) is not significant. Among the remaining 56 per cent open clusters with |Rg − Rb| > 1 kpc, young ones with t < 1.0 Gyr tend to migrate inward, while older clusters usually migrate outward. Different mechanisms of radial migration between young and old clusters are suggested based on their different migration rates, Galactic locations, and orbital parameters. For the old group, we propose a plausible way to estimate migration rate and obtain a reasonable value of $1.5 \pm 0.5 \,\rm {kpc\,Gyr}^{ -1}$ based on 10 intermediate-age clusters at the outer disc, where the existence of several special clusters implies its complicate formation history.

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