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\n  \n 2023\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n GAMMA: Galactic Attributes of Mass, Metallicity, and Age Dataset.\n \n \n \n \n\n\n \n Çakır, U.; and Buck, T.\n\n\n \n\n\n\n 2023.\n \n\n\n\n
\n\n\n\n \n \n \"GAMMA: paper\n  \n \n \n \"GAMMA: code\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{cakir2023gamma,\n    title={GAMMA: Galactic Attributes of Mass, Metallicity, and Age Dataset}, \n    author={Ufuk Çakır and Tobias Buck},\n    year={2023},\n    eprint={2312.06016},\n    archivePrefix={arXiv},\n    primaryClass={astro-ph.GA},\n    url_Paper = {https://arxiv.org/abs/2312.06016},\n    url_Code = {https://github.com/ufuk-cakir/GAMMA},\nabstract = {We introduce the GAMMA (Galactic Attributes of Mass, Metallicity, and Age) dataset, a comprehensive collection of galaxy data tailored for Machine Learning applications. This dataset offers detailed 2D maps and 3D cubes of 11 727 galaxies, capturing essential attributes: stellar age, metallicity, and mass. Together with the dataset, we publish our code to extract any other stellar or gaseous property from the raw simulation suite to extend the dataset beyond these initial properties, ensuring versatility for various computational tasks. Ideal for feature extraction, clustering, and regression tasks, GAMMA offers a unique lens to explore galactic structures using computational methods and is a bridge between astrophysical simulations and the field of scientific machine learning (ML). As a first benchmark, we applied Principal Component Analysis (PCA) to this dataset. We find that PCA effectively captures the key morphological features of galaxies with a small number of components. We achieve a dimensionality reduction by a factor of approximately 200 (3650) for 2D images (3D cubes) with a reconstruction accuracy below 5%.}\n}\n
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\n We introduce the GAMMA (Galactic Attributes of Mass, Metallicity, and Age) dataset, a comprehensive collection of galaxy data tailored for Machine Learning applications. This dataset offers detailed 2D maps and 3D cubes of 11 727 galaxies, capturing essential attributes: stellar age, metallicity, and mass. Together with the dataset, we publish our code to extract any other stellar or gaseous property from the raw simulation suite to extend the dataset beyond these initial properties, ensuring versatility for various computational tasks. Ideal for feature extraction, clustering, and regression tasks, GAMMA offers a unique lens to explore galactic structures using computational methods and is a bridge between astrophysical simulations and the field of scientific machine learning (ML). As a first benchmark, we applied Principal Component Analysis (PCA) to this dataset. We find that PCA effectively captures the key morphological features of galaxies with a small number of components. We achieve a dimensionality reduction by a factor of approximately 200 (3650) for 2D images (3D cubes) with a reconstruction accuracy below 5%.\n
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