arXiv subjects

A. Castro-Ginard

Publications and source records attributed to A. Castro-Ginard.

26 records · Page 2Linked to original sources

arXiv2020

Hunting for open clusters in \textit{Gaia} DR2: $582$ new OCs in the Galactic disc

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 $(l,b,\varpi,μ_{α^*},μ_δ)$. 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.

astro-ph.GA↗

arXiv2019

Gaia kinematics reveal a complex lopsided and twisted Galactic disc warp

There are few warp kinematic models of the Galaxy able to characterise structure and kinematics. These models are necessary to study the lopsidedness of the warp and the twisting of the line-of-nodes of the stellar warp, already seen in gas and dust. We use the \Gaia~Data Release 2 astrometric data up to $G=20$mag to characterise the structure of the Galactic warp, the vertical motions and the dependency on the age. We use two populations up to galactocentric distances of $16$kpc, a young (OB-type) and an old (Red Giant Branch, RGB). We use the nGC3 PCM and LonKin methods based on the Gaia observables, together with 2D projections of the positions and proper motions in the Galactic plane. We confirm the age dependency of the Galactic warp, both in positions and kinematics, being the height of the Galactic warp of about $0.2$kpc for the OB sample and of $1.$kpc for the RGB at a galactocentric distance of $14$kpc. Both methods find that the onset radius is $12\sim 13$kpc for the OB sample and $10\sim 11$kpc for the RGB. From the RGB sample, we find from galactocentric distances larger than $10$kpc the line-of-nodes twists away from the Sun-anticentre line towards galactic azimuths $\sim 180-200^{\circ}$ increasing with radius, though possibly influenced by extinction. The RGB sample reveals a slightly lopsided stellar warp with $\sim 250$pc between the up and down sides. The line of maximum of proper motions in latitude is systematically offset from the line-of-nodes estimated from the spatial data, which our models predict as a kinematic signature of lopsidedness. We also show a prominent wave-like pattern of a bending mode different in the OB and RGB, and substructures that might not be related to the Galactic warp nor to a bending mode. GDR2 triggers the need for complex kinematic models, flexible enough to combine both wave-like patterns and an S-shaped lopsided warp.[abridged]

astro-ph.GA↗

arXiv2019

Hunting for open clusters in \textit{Gaia} DR2: the Galactic anticentre

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. We systematically scan Gaia DR2 in a region covering the Galactic anticentre and the Perseus arm $(120 \leq l \leq 205$ and $-10 \leq b \leq 10)$, with the goal of finding any open clusters that may exist in this region, and fine tuning a previously proposed methodology successfully applied to TGAS data, adapting it to different density regions. Our methodology uses an unsupervised, density-based, clustering algorithm, DBSCAN, that identifies overdensities in the five-dimensional astrometric parameter space $(l,b,\varpi,μ_{α^*},μ_δ)$ 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. 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. 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.

astro-ph.GA↗

arXiv2019

Gaia DR2 unravels incompleteness of nearby cluster population: New open clusters in the direction of Perseus

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.

astro-ph.GA↗

arXiv2019

Open cluster kinematics with Gaia DR2

Context. Open clusters are very good tracers of the evolution of the Galactic disc. Thanks to Gaia, their kinematics can be investigated with an unprecedented precision and accuracy. Aims. The distribution of open clusters in the 6D phase space is revisited with Gaia DR2. Methods. The weighted mean radial velocity of open clusters was determined, using the most probable members available from a previous astrometric investigation that also provided mean parallaxes and proper motions. Those parameters, all derived from Gaia DR2 only, were combined to provide the 6D phase space information of 861 clusters. The velocity distribution of nearby clusters was investigated, as well as the spatial and velocity distributions of the whole sample as a function of age. A high quality subsample was used to investigate some possible pairs and groups of clusters sharing the same Galactic position and velocity. Results. For the high quality sample that has 406 clusters, the median uncertainty of the weighted mean radial velocity is 0.5 km/s. The accuracy, assessed by comparison to ground-based high resolution spectroscopy, is better than 1 km/s. Open clusters nicely follow the velocity distribution of field stars in the close Solar neighbourhood previously revealed by Gaia DR2. As expected, the vertical distribution of young clusters is very flat but the novelty is the high precision to which this can be seen. The dispersion of vertical velocities of young clusters is at the level of 5 km/s. Clusters older than 1 Gyr span distances to the Galactic plane up to 1 kpc with a vertical velocity dispersion of 14 km/s, typical of the thin disc. Five pairs of clusters and one group with five members are possibly physically related. Other binary candidates previously identified turn out to be chance alignment.

astro-ph.SR↗

arXiv2018

A Gaia DR2 view of the Open Cluster population in the Milky Way

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 sub-milliarcsecond level and homogeneous photometry at the mmag level, that can be used to characterise a large number of clusters over the entire sky. In this study we aim to a establish list of members and derive mean parameters, in particular distances, for as many clusters as possible, making use of Gaia data alone. We compile a list of thousands of known or putative clusters from the literature. We then apply an unsupervised membership assignment code, UPMASK, to the Gaia DR2 data contained within the fields of those clusters. 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 GaiaDR2 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.

astro-ph.GA↗

arXiv2018

A new method for unveiling Open Clusters in Gaia: new nearby Open Clusters confirmed by DR2

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 ($l$, $b$, $\varpi$, $μ_{α^*}$, $μ_δ$) 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.

astro-ph.GA↗

arXiv2018

Gaia Data Release 2: using Gaia parallaxes

The second Gaia data release (GDR2) provides precise five-parameter astrometric data (positions, proper motions and parallaxes) for an unprecedented amount of sources (more than $1.3$ billion, mostly stars). The use of this wealth of astrometric data comes with a specific challenge: how does one properly infer from these data the astrophysical parameters of interest? The main - but not only - focus of this paper is the issue of the estimation of distances from parallaxes, possibly combined with other information. We start with a critical review of the methods traditionally used to obtain distances from parallaxes and their shortcomings. Then we provide guidelines on how to use parallaxes more efficiently to estimate distances by using Bayesian methods. In particular also we show that negative parallaxes, or parallaxes with relatively larger uncertainties still contain valuable information. Finally, we provide examples that show more generally how to use astrometric data for parameter estimation, including the combination of proper motions and parallaxes and the handling of covariances in the uncertainties. The paper contains examples based on simulated Gaia data to illustrate the problems and the solutions proposed. Furthermore, the developments and methods proposed in the paper are linked to a set of tutorials included in the Gaia archive documentation that provide practical examples and a good starting point for the application of the recommendations to actual problems. In all cases the source code for the analysis methods is provided. Our main recommendation is to always treat the derivation of (astro-) physical parameters from astrometric data, in particular when parallaxes are involved, as an inference problem which should preferably be handled with a full Bayesian approach.

astro-ph.IM↗