Galaxy Morphological Classification with Efficient Vision Transformer
  Quantifying the morphology of galaxies has been an important task in
astrophysics to understand the formation and evolution of galaxies. In recent
years, the data size has been dramatically increasing due to several on-going
and upcoming surveys. Labeling and identifying interesting objects for further
investigations has been explored by citizen science through the Galaxy Zoo
Project and by machine learning in particular with the convolutional neural
networks (CNNs). In this work, we explore the usage of Vision Transformer (ViT)
for galaxy morphology classification for the first time. We show that ViT could
reach competitive results compared with CNNs, and is specifically good at
classifying smaller-sized and fainter galaxies. With this promising preliminary
result, we believe the ViT network architecture can be an important tool for
galaxy morphological classification for the next generation surveys. Our open
source, is publicly available at
  \url{https://github.com/sliao-mi-luku/Galaxy-Zoo-Classification}
