Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userGaia DR2 unravels incompleteness of nearby cluster population: New open clusters in the direction of Perseus
68
0
0.0
(
0
)
Added by
Tristan Cantat-Gaudin
Publication date
2018
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Open clusters (OCs) are popular tracers of the structure and evolutionary history of the Galactic disk. The OC population is often considered to be complete within 1.8 kpc of the Sun. The recent Gaia Data Release 2 (DR2) allows the latter claim to be challenged. We perform a systematic search for new OCs in the direction of Perseus using precise and accurate astrometry from Gaia DR2. We implement a coarse-to-fine search method. First, we exploit spatial proximity using a fast density-aware partitioning of the sky via a k-d tree in the spatial domain of Galactic coordinates, (l, b). Secondly, we employ a Gaussian mixture model in the proper motion space to quickly tag fields around OC candidates. Thirdly, we apply an unsupervised membership assignment method, UPMASK, to scrutinise the candidates. We visually inspect colour-magnitude diagrams to validate the detected objects. Finally, we perform a diagnostic to quantify the significance of each identified overdensity in proper motion and in parallax space We report the discovery of 41 new stellar clusters. This represents an increment of at least 20% of the previously known OC population in this volume of the Milky Way. We also report on the clear identification of NGC 886, an object previously considered an asterism. This letter challenges the previous claim of a near-complete sample of open clusters up to 1.8 kpc. Our results reveal that this claim requires revision, and a complete census of nearby open clusters is yet to be found.
rate research
Read More
The publication of the Gaia Data Release 2 (Gaia DR2) opens a new era in Astronomy. It includes precise astrometric data (positions, proper motions and parallaxes) for more than $1.3$ billion sources, mostly stars. To analyse such a vast amount of new data, the use of data mining techniques and machine learning algorithms are mandatory. The search for Open Clusters, groups of stars that were born and move together, located in the disk, is a great example for the application of these techniques. Our aim is to develop a method to automatically explore the data space, requiring minimal manual intervention. We explore the performance of a density based clustering algorithm, DBSCAN, to find clusters in the data together with a supervised learning method such as an Artificial Neural Network (ANN) to automatically distinguish between real Open Clusters and statistical clusters. The development and implementation of this method to a $5$-Dimensional space ($l$, $b$, $varpi$, $mu_{alpha^*}$, $mu_delta$) to the Tycho-Gaia Astrometric Solution (TGAS) data, and a posterior validation using Gaia DR2 data, lead to the proposal of a set of new nearby Open Clusters. We have developed a method to find OCs in astrometric data, designed to be applied to the full Gaia DR2 archive.
We report the serendipitous discovery of three new open clusters, named UFMG 1, UFMG 2 and UFMG 3 in the field of the intermediate-age cluster NGC 5999, by using Gaia DR2 data. A colour-magnitude filter tailored for a proper selection of main-sequence stars and red clump giants turned evident the presence of NGC 5999 and these three new stellar groups in proper motion space. Their structural parameters were derived from King-profile fittings over their projected stellar distributions and isochrone fits were performed on the clusters cleaned colour-magnitude diagrams built with Gaia bands to derive their astrophysical parameters. The clusters projected sky motion were calculated for each target using our members selection. Distances to the clusters were inferred from stellar parallaxes through a bayesian model, showing that they are marginally consistent with their isochronal distances, considering the random and systematic uncertainties involved. The new clusters are located in the nearby Sagittarius arm (d ~ 1.5 kpc) with NGC 5999 at the background (d ~ 1.8 kpc). They contain at least a few hundred stars of nearly solar metallicity and have ages between 100 and 1400 Myr.
Very precise observational data are needed for studying the stellar cluster parameters (distance, reddening, age, metallicity) and cluster internal kinematics. In turn, these give us an insight into the properties of our Galaxy, for example, by giving us the ability to trace Galactic spiral structure, star formation rates and metallicity gradients. We investigated the available Gaia DR2 catalogue of 1229 open clusters and studied cluster distances, sizes and membership distributions in the 3D space. An appropriate analysis of the parallaxto-distance transformation problem is presented in the context of getting distances toward open clusters and estimating their sizes. Based on our investigation of the Gaia DR2 data we argue that, within 2 kpc, the inverse-parallax method gives comparable results (distances and sizes) as the Bayesian approach based on the exponentially decreasing volume density prior. Both of these methods show very similar dependence of the line-of-sight elongation of clusters (needle-like shapes resulting from the parallax uncertainties) on the distance. We also looked at a measure of elongations of the studied clusters and find the maximum distance of 665 pc at which a spherical fit still contains about half of the stellar population of a cluster. It follows from these results that the 3D structure of an open cluster cannot be properly studied beyond about 500 pc when using any of mentioned standard transformations of parallaxes to distances.
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.
Blue straggler stars (BSS) are well studied in globular clusters but their systematic study with secure membership determination is lacking in open clusters. We use Gaia DR2 data to determine accurate stellar membership for four intermediate-age open clusters, Melotte 66, NGC 2158, NGC 2506 and NGC 6819, and three old open clusters, Berkeley 39, NGC 188 and NGC 6791, to subsequently study their BSS populations. The BSS radial distributions of five clusters, Melotte 66, NGC 188, NGC 2158, NGC 2506, and NGC 6791, show bimodal distributions, placing them with Family II globular clusters which are of intermediate dynamical ages. The location of minima, $r_mathrm{{min}}$, in the bimodal BSS radial distributions, varies from 1.5$r_c$ to 4.0$r_c$, where $r_c$ is the core radius of the clusters. We find a positive correlation between $r_mathrm{{min}}$ and $N_{mathrm{relax}}$, the ratio of cluster age to the current central relaxation time of the cluster. We further report that this correlation is consistent in its slope, within the errors, to the slope of the globular cluster correlation between the same quantities, but with a slightly higher intercept. This is the first example in open clusters that shows BSS radial distributions as efficient probes of dynamical age. The BSS radial distributions of the remaining two clusters, Berkeley 39 and NGC 6819, are flat. The estimated $N_{mathrm{relax}}$ values of these two clusters, however, indicate that they are dynamically evolved. Berkeley 39 especially has its entire BSS population completely segregated to the inner regions of the cluster.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
