利用无监督矢量量化变分自编码器探测 MeerKAT 射电连续辐射数据中的神秘射电源

本文提出了一种利用矢量量化变分自编码器（VQ-VAE）作为无监督工具，对 MeerKAT 星系团遗留巡天（MGCLS）的 1.28 GHz 射电连续辐射图像进行探测的方法。研究表明，该方法在无需标注数据的情况下，能够有效识别像平面中具有异常结构的射电连续辐射源，且在处理速度和性能上具有显著优势。

arXiv:2601.13901v2 Announce Type: replace Abstract: We present a novel application of Vector quantised variational autoencoders (VQ-VAEs) as an unsupervised tool to probe deep 1.28 GHz radio continuum images taken from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS) and compare their performance to other machine learning methods. We examine the effectiveness of VQ-VAEs in identifying radio continuum sources with anomalous structures in the image-plane domain. We compare performance to a supervised training set and other published anomaly detection methods, focusing especially on simple autoencoders (AE), Memory Unit Autoencoders (MUAE) and a human-in-the-loop software Astronomaly based on bootstrap your own latent (BYOL). Our investigations show that VQ-VAE perform better than simple AEs, are not as accurate as MUAE but are much faster and are also faster and comparable to Astronomaly while not requiring labelled data. We observe that they are able to remove a majority of the typical sources in such data, even when trained in an unsupervised manner on unlabelled data. We also provide our testing set of a large sample of manually labelled radio sources, in particular radio galaxies, taken from the MGCLS at 1.28 GHz. We find VQ-VAEs to be potentially useful as part of automated approaches towards searching