The increasing amount of data in astronomy provides great challenges for machine learning research. Previously, supervised learning methods achieved satisfactory recognition accuracy for the star-galaxy classification task, based on manually labeled data set. In this work, we propose a novel unsupervised approach for the star-galaxy recognition task, namely Cascade Variational Auto-Encoder (CasVAE). Our empirical results show our method outperforms the baseline model in both accuracy and stability.
@article{arxiv.1910.14056,
title = {Unsupervised Star Galaxy Classification with Cascade Variational Auto-Encoder},
author = {Hao Sun and Jiadong Guo and Edward J. Kim and Robert J. Brunner},
journal= {arXiv preprint arXiv:1910.14056},
year = {2019}
}