Beyond the hubble sequence - exploring galaxy morphology with unsupervised machine learning
We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantized variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that includes: (1) consideration of the clustering performance simultaneously when learning features from images; (2
Cheng, Ting-Yun et al.
Fecha de publicación:
5
2021