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We present a novel approach for the dimensionality reduction of galaxy imagesby leveraging a combination of variational auto-encoders (VAE) and domainadaptation (DA). We demonstrate the effectiveness of this approach using asample of low redshift galaxies with detailed morphological type labels fromthe Galaxy-Zoo DECaLS project. We show that 40-dimensional latent variables caneffectively reproduce most morphological features in galaxy images. To furthervalidate the effectiveness of our approach, we utilised a classical randomforest (RF) classifier on the 40-dimensional latent variables to make detailedmorphology feature classifications. This approach performs similarly to adirect neural network application on galaxy images. We further enhance ourmodel by tuning the VAE network via DA using galaxies in the overlappingfootprint of DECaLS and BASS+MzLS, enabling the unbiased application of ourmodel to galaxy images in both surveys. We observed that DA led to even bettermorphological feature extraction and classification performance. Overall, thiscombination of VAE and DA can be applied to achieve image dimensionalityreduction, defect image identification, and morphology classification in largeoptical surveys.

From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation