Research Division Seminar
Revealing Galaxy Morphology with Spectral Data and Unsupervised Techniques

Dr. José Antonio de Diego Onsurbe

Resumen

Using unsupervised machine learning methods, we present a novel approach to classifying galaxies into early and late types based on their spectral characteristics. The research utilizes a balanced dataset of 2000 galaxies from the Galaxy Zoo 2 and spectral data from the Sloan Digital Sky Survey Data Release 13. The methodology involves applying an Autoencoder Neural Network for dimensionality reduction, followed by a Gaussian Mixture Model for clustering. The study demonstrates that this approach achieves an accuracy rate of approximately 86% in galaxy classification, highlighting the potential of unsupervised machine learning techniques in enhancing the precision and efficiency of morphological classification of galaxies based on spectral data.

 

Sobre la charla

Revealing Galaxy Morphology with Spectral Data and Unsupervised Techniques
Dr. José Antonio de Diego Onsurbe
jdo@astro.unam.mx
Tuesday January 23, 2024 - 12:30 GMT  (Online)
en     en
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