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COSMIC: Characterization Of Star clusters using Machine-learning Inference and Clustering

Overview

COSMIC is an open-source software suite built on Python 3.11 and the PyMC library. It employs unsupervised machine learning techniques and Bayesian estimation to analyze extensive datasets from the Gaia satellite, focusing on open star clusters. The software aims to accurately identify key parameters such as structural attributes, reddening, age, and distance.

Note: This project is under heavy development and is not recommended for scientific work at this time.

Contributors

  • MSc. Lucas Pulgar-Escobar, Universidad de Concepción, Chile
  • Nicolás Henríquez Salgado, Universidad de Concepción, Chile

License

COSMIC is licensed under the AGPL-3.0 License. Please see the LICENSE.md file for more details.

Installation

Coming Soon: Detailed installation instructions.

Usage

Coming Soon: A quick start guide and usage examples.

Dependencies

COSMIC relies on a variety of Python packages for its functionality. For Bayesian modeling, we use the PyMC library. Clustering capabilities are powered by HDBSCAN. Data visualization and statistical graphics are implemented using matplotlib and seaborn. For astronomical data handling, we utilize astropy.

Acknowledgments

We would like to extend our gratitude to the developers behind PyMC and HDBSCAN for their invaluable libraries, which form the backbone of COSMIC's Bayesian and clustering functionalities, respectively.

Contributing

Interested in contributing? Please refer to the CONTRIBUTING.md file for guidelines.

Support and Contact

For questions or support, please open an issue on our GitHub page. Alternatively, you can contact Lucas Pulgar-Escobar at [email protected].