arXiv 2105.024342021-05astro-ph.GAastro-ph.IM

用生成对抗网络检测Hyper Suprime-Cam星系图像中的异常

Anomaly detection in Hyper Suprime-Cam galaxy images with generative adversarial networks

Kate Storey-Fisher, Marc Huertas-Company, Nesar Ramachandra, Francois Lanusse, Alexie Leauthaud, Yifei Luo, Song Huang, J. Xavier Prochaska

备注：: Published in MNRAS
发表信息：: Monthly Notices of the Royal Astronomical Society, Volume 508, Issue 2, December 2021, Pages 2946-2963
DOI：: 10.1093/mnras/stab2589

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摘要：

随着数据集大小的增加，天文测量中的异常检测问题变得越来越重要。我们提出了一种无监督的异常检测方法的结果，使用Wasserstein生成对抗网络（WGAN）在近一百万个光学星系图像的超Suprime-Cam（HSC）调查。WGAN学习生成遵循数据集分布的真实HSC类星系;异常图像是基于生成器的不良重建和由WGAN学习的外围特征定义的。我们发现，与生成器相比，与更简单的基于自动编码器的异常检测方法相比，鉴别器更适合于潜在的有趣异常，因此我们使用鉴别器选择的图像来构建$\sim$ 13，000个对象的高异常样本。我们提出了一种新的方法来进一步表征这些异常图像：我们使用卷积自动编码器来降低真实图像和WGAN重建图像之间的残差的维数，并对这些图像进行UMAP聚类。我们报告检测到的异常的利益，包括星系合并，潮汐功能，和极端的恒星形成星系。附录中详细描述了对其中一个异常的后续光谱分析;我们发现这是一个不寻常的系统，最有可能是一个贫金属的矮星系，具有极蓝色的高金属丰度HII区域。我们已经发布了一个包含WGAN异常分数的目录;代码和目录可以在https：//github.com/kstoreyf/anomalies-GAN-HSC上找到，我们用于探索集群数据的交互式可视化工具可以在https://weirdgalaxi.es上找到。

英文摘要：

The problem of anomaly detection in astronomical surveys is becoming increasingly important as data sets grow in size. We present the results of an unsupervised anomaly detection method using a Wasserstein generative adversarial network (WGAN) on nearly one million optical galaxy images in the Hyper Suprime-Cam (HSC) survey. The WGAN learns to generate realistic HSC-like galaxies that follow the distribution of the data set; anomalous images are defined based on a poor reconstruction by the generator and outlying features learned by the discriminator. We find that the discriminator is more attuned to potentially interesting anomalies compared to the generator, and compared to a simpler autoencoder-based anomaly detection approach, so we use the discriminator-selected images to construct a high-anomaly sample of $\sim$13,000 objects. We propose a new approach to further characterize these anomalous images: we use a convolutional autoencoder to reduce the dimensionality of the residual differences between the real and WGAN-reconstructed images and perform UMAP clustering on these. We report detected anomalies of interest including galaxy mergers, tidal features, and extreme star-forming galaxies. A follow-up spectroscopic analysis of one of these anomalies is detailed in the Appendix; we find that it is an unusual system most likely to be a metal-poor dwarf galaxy with an extremely blue, higher-metallicity HII region. We have released a catalog with the WGAN anomaly scores; the code and catalog are available at https://github.com/kstoreyf/anomalies-GAN-HSC, and our interactive visualization tool for exploring the clustered data is at https://weirdgalaxi.es.

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