Master Thesis

The characterization and understanding of Open Clusters (OCs) allow us to
understand better properties and mechanisms about the Universe such as stellar
formation and the regions where these events occur. They also provide information
about stellar processes and the evolution of the galactic disk.

In this work, we present a novel method to characterize OCs. Our method employs a
model built on Artificial Neural Networks (ANNs). More specifically, we adapted
a state of the art model, the Deep Embedded Clustering (DEC) model for our purpose.
The developed method aims to improve classical state of the arts techniques. We improved
not only in terms of computational efficiency (with lower computational requirements),
but in usability (reducing the number of hyperparameters to get a good characterization
of the analyzed clusters). For our experiments, we used the Gaia DR2 database as
the data source, and compared our model with the clustering technique K-Means. Our
method achieves good results, becoming even better (in some of the cases) than current techniques.