This repository contains the code and datasets for the research project "Klasifikacija nebesnih teles z uporabo strojnega učenja" (Classification of Celestial Bodies Using Machine Learning). The project aims to analyze and classify various celestial bodies using advanced machine learning techniques. By leveraging a diverse dataset that includes photographs and spectroscopic data among other measurable information, we aim to develop a model capable of predicting and analyzing numerous properties of celestial bodies, including those not yet identified.

For an interactive experience and to dive directly into the coding and analysis part, click the button below to access our Google Colab notebook:

This repository is part of an ongoing research paper aimed at advancing the field of astronomical classification through machine learning. The research explores novel methodologies for classifying celestial bodies based on their spectral data, images, and other measurable characteristics.

This project is open-sourced under the MIT License. See the LICENSE file for more details.

For more information on how to contribute to this project or any inquiries, please open an issue or submit a pull request.

@article{Walmsley2023, doi = {10.21105/joss.05312}, url = {https://doi.org/10.21105/joss.05312}, year = {2023}, publisher = {The Open Journal}, volume = {8}, number = {85}, pages = {5312}, author = {Mike Walmsley and Campbell Allen and Ben Aussel and Micah Bowles and Kasia Gregorowicz and Inigo Val Slijepcevic and Chris J. Lintott and Anna M. m. Scaife and Maja Jabłońska and Kosio Karchev and Denise Lanzieri and Devina Mohan and David O’Ryan and Bharath Saiguhan and Crisel Suárez and Nicolás Guerra-Varas and Renuka Velu}, title = {Zoobot: Adaptable Deep Learning Models for Galaxy Morphology}, journal = {Journal of Open Source Software} }