一种针对检测弥漫无线电发射的机器学习方法：Astronomaly: Protege
A targeted machine learning approach for detecting diffuse radio emission with Astronomaly: Protege
AI总结：
本文提出了一种基于主动学习的机器学习方法，利用Astronomaly: Protege框架高效检测银河团中的弥漫无线电发射，显著降低人工标注需求。
摘要：
银河团中的弥漫无线电发射，如无线电晕、遗迹和微型晕，是非热过程、湍流和磁场在团内介质中的关键示踪剂。然而，它们的低表面亮度，以及来自致密源和成像伪影的污染，使得它们的检测具有挑战性。来自如平方千米阵（SKA）等仪器的数据量庞大，将使基于传统手动检查的检测方法不可行。本文介绍了一种新颖的机器学习方法，利用主动学习快速从少量最优选择的数据子集中识别弥漫发射候选者。我们应用自监督深度学习算法Bootstrap Your Own Latent，从 MeerKAT 银河团遗产调查（MGCLS）中的源裁剪中提取特征。然后将这些特征通过 Astronomaly: Protege 异常检测框架传递以确定最终候选者。使用人类标注的数据集，我们在高分辨率（约7'''）、卷积（15'''）和结合特征的 MGCLS 数据集中评估我们的流程。有趣的是，高分辨率特征比卷积分辨率更有效地识别弥漫源，而后者又被结合特征所超越。在 Protege 排名前100的源中，99%表现出弥漫特征，其中55%被确认为团相关发射。我们的工作表明，Protege 可以在极少量的人类标注努力下识别弥漫发射，提供了一种强大且可扩展的工具，能够检测已知和新型的弥漫无线电源。
英文摘要：
Diffuse radio emission in galaxy clusters, such as radio halos, relics, and mini halos, is a key tracer of non-thermal processes, turbulence, and magnetic fields within the intra-cluster medium. However, their low surface brightness, as well as contamination from compact sources and imaging artefacts, makes their detection challenging. The sheer volume of data from instruments such as the Square Kilometre Array will render traditional manual-inspection based detection methods infeasible. This paper introduces a novel machine learning approach that uses active learning to rapidly identify diffuse emission candidates from a small, optimally-selected subset of data. We apply the self-supervised deep learning algorithm Bootstrap Your Own Latent to extract features from source cutouts in the MeerKAT Galaxy Cluster Legacy Survey (MGCLS). We then pass these features through the Astronomaly: Protege anomaly detection framework to identify the final candidates. Using a human-labelled set, we evaluate our pipeline on high-resolution (~ 7''), convolved (15''), and combined-feature MGCLS datasets. Interestingly, the high-resolution features identify diffuse sources more efficiently than the convolved resolution, which are in turn outperformed by the combined features. Of the top 100 sources ranked by Protege, 98% exhibit diffuse characteristics, with 53% confirmed as cluster-related emission. Our work shows that Protege can identify diffuse emission with minimal human labelling effort, offering a powerful, scalable tool capable of detecting both known and novel diffuse radio sources.
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