Paper (UNIR Style)

Characterize and understand 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 paper, 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.

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.

Release Candidate

Thesis

• Improve Melotte 25 results
• Add paper as an appendix
• Other minor corrections

Paper

• Initial version

Third Draft

Code

• Add ML utils
• Add clusterix_topcat.ipynb

Thesis

• Update Introduction and State of the Art chapters
• Update Method chapter
• Add Cluster Characterization with VO Tools section
• Add Results chapter
• Add Conclusions chapter

Second Draft

Code

• Implemented initial version of Unsupervised Deep Embedding for Clustering Analysis (DEC)
• Improved Gaia downloader

Thesis

• Added Method chapter
• Updated figures
• Expanded Introduction and State of the Art chapters
• Rewritten Aims chapter (still under development...)

Initial Draft

Code

• Initial version of the Gaia downloader
• Implemented data models

Data

• Downloaded stars for clusters listed inside OpenClust catalogue

Thesis

• Added Introduction, State of the Art and Aims chapters
• Added references