2020/12/14 by Kate Storey-Fisher, Marc Huertas-Company, Storey-Fisher, Kate +11
Computer Science · #Advanced Image Processing Techniques #Anomaly Detection Techniques and Applications #Astrophysics of Galaxies (astro-ph.GA) #Digital Media Forensic Detection #FOS: Physical sciences
paper · pdf · doi:10.48550/arxiv.2012.08082
openalex publication_date 2020/12/15 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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 Hawai'i. 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 generator's 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.