We present a novel approach for the dimensionality reduction of galaxy images by leveraging a combination of variational auto-encoders (VAE) and domain adaptation (DA). We demonstrate the effectiveness of this approach using a sample of low redshift galaxies with detailed morphological type labels from the Galaxy-Zoo DECaLS project. We show that 40-dimensional latent variables can effectively reproduce most morphological features in galaxy images. To further validate the effectiveness of our approach, we utilised a classical random forest (RF) classifier on the 40-dimensional latent variables to make detailed morphology feature classifications. This approach performs similarly to a direct neural network application on galaxy images. We further enhance our model by tuning the VAE network via DA using galaxies in the overlapping footprint of DECaLS and BASS+MzLS, enabling the unbiased application of our model to galaxy images in both surveys. We observed that noise suppression during DA led to even better morphological feature extraction and classification performance. Overall, this combination of VAE and DA can be applied to achieve image dimensionality reduction, defect image identification, and morphology classification in large optical surveys.
翻译:为进一步验证我们的方法的有效性,我们利用40维潜伏变量上的经典随机森林分类法来进行详细的形态特征分类。这种方法与星系图像的直接神经网络应用类似。我们通过银河-动物DECALS和BASS+MZLS的重叠足迹,通过DA通过星系调整VAE网络,我们进一步加强了我们的模型,在DECALS和BASS+MZLS的重叠足迹中,我们利用这些星系和BASS+MZLS的星系足迹,使我们的模型能够对星系图像进行公正的应用。我们观察到,在DA期间,噪音抑制导致更佳的形态特征提取和分类性能。总体而言,VAE和DA的这种组合可以用于在大型光学测量中实现图像尺寸减少、缺陷图像识别和形态分类。</s>