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We present an anomaly detection method using Wasserstein generative adversarial networks (WGANs) on optical galaxy images from the wide-field survey conducted with the Hyper Suprime-Cam (HSC) on the Subaru Telescope in Hawaii. The WGAN is trained on the entire sample, and learns to generate realistic HSC-like images that follow the distribution of the training data. We identify images which are less well-represented in the generators latent space, and which the discriminator flags as less realistic; these are thus anomalous with respect to the rest of the data. We propose a new approach to characterize these anomalies based on a convolutional autoencoder (CAE) to reduce the dimensionality of the residual differences between the real and WGAN-reconstructed images. We construct a subsample of ~9,000 highly anomalous images from our nearly million object sample, and further identify interesting anomalies within these; these include galaxy mergers, tidal features, and extreme star-forming galaxies. The proposed approach could boost unsupervised discovery in the era of big data astrophysics.
With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative networks for detecting outliers in astronomical imaging datasets. The mai
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