The aim is to search for galaxies with similar properties. We namely them twin galaxies.
Author: Carlos García Peral@carlosgp-ai
Co-Author and Advisor: Guzmán-Álvarez, César A. @cguz
A novel method to detect similarities between galaxies. We based on different deep learning models to build the most efficient model. The innovative lies in the fact that the vast majority of works are based on the classification and detection of galaxies. Our approach is different, not seeking classification as the final objective but rather the search for galaxies with similar properties. We namely them twin galaxies.
Our approach, first, classifies the galaxies by their morphology, and then based on a Convolutional Neural Network (CNN), compares the feature vectors of the galaxies (same as the works done by Victor and Miguel) and from those vectors, we calculate the Euclidean distance establishing a ranking that will indicate the twin galaxies. We train and test our models using SDSS images for objects in the CALIFA SURVEY.
Author: Víctor Zamora Abarca
Co-Author and Advisor: Guzmán-Álvarez, César A. @cguz
Author: Miguel López Marín
Co-Author and Advisor: Guzmán-Álvarez, César A. @cguz
Three Master Thesis degrees:
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Automation in the detection of similarities between galaxies with Deep Learning processes
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Detection of similarities between galaxies using machine learning algorithms
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Detection of similarities between galaxies using Artificial Intelligence
Working on a conference paper: