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

TL;DR

This paper introduces a novel automated method combining density-based clustering and neural networks to identify new nearby Open Clusters in Gaia DR2 data, significantly advancing star cluster detection techniques.

Contribution

The paper presents a new automated approach using DBSCAN and ANN to efficiently detect and validate Open Clusters in Gaia's vast astrometric dataset, requiring minimal manual effort.

Findings

Successfully identified new nearby Open Clusters in Gaia DR2 data.

Developed a scalable method applicable to the full Gaia archive.

Validated the method with existing data, confirming its effectiveness.

Abstract

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.