在过去的几年里，机器学习技术在天文学应用中越来越有用，例如在星系的形态分类中。卷积神经网络已被证明在对图像数据中的对象进行分类方面非常有效。目前的工作旨在确定在射电源综合成像观测的天文学背景下，何时存在多个组成部分。为此，我们设计了一个卷积神经网络，使用来自Radio Galaxy Zoo（RGZ）公民科学项目的来源来区分不同的形态类别。在第一步中，我们专注于探索影响此类神经网络性能的因素，例如训练数据量，层数和性质以及超参数。我们从一个简单的实验开始，我们只区分两个极端的形态，使用紧凑和多组件扩展源。我们发现，三卷积层架构产生了非常好的结果，在测试数据集上实现了97.4%的分类准确率。然后在一个四类问题上测试了相同的架构，我们让网络将源分类为紧凑型和三类扩展源，测试准确率达到93.5%。性能最佳的卷积神经网络设置已在RGZ Data Release 1中进行了验证，其中使用原始图像和增强图像获得了94.8%的最终测试准确度。使用sigma裁剪总体上并没有提供显著的好处，除了在训练图像数量较少的情况下。
英文摘要：
Machine learning techniques have been increasingly useful in astronomical applications over the last few years, for example in the morphological classification of galaxies. Convolutional neural networks have proven to be highly effective in classifying objects in image data. The current work aims to establish when multiple components are present, in the astronomical context of synthesis imaging observations of radio sources. To this effect, we design a convolutional neural network to differentiate between different morphology classes using sources from the Radio Galaxy Zoo (RGZ) citizen science project. In this first step, we focus on exploring the factors that affect the performance of such neural networks, such as the amount of training data, number and nature of layers and the hyperparameters. We begin with a simple experiment in which we only differentiate between two extreme morphologies, using compact and multiple component extended sources. We found that a three convolutional layer architecture yielded very good results, achieving a classification accuracy of 97.4% on a test data set. The same architecture was then tested on a four-class problem where we let the network classify sources into compact and three classes of extended sources, achieving a test achieving a test accuracy of 93.5%. The best-performing convolutional neural network setup has been verified against RGZ Data Release 1 where a final test accuracy of 94.8% was obtained, using both original and augmented images. The use of sigma clipping does not offer a significant benefit overall, except in cases with a small number of training images.
Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images
Western Sydney University（西悉尼大学）；CSIRO Astronomy and Space Science（CSIRO天文与空间科学部）；The Australian National University（澳大利亚国立大学）；Data61, CSIRO（CSIRO Data61）；Universidad de Guanajuato（瓜纳华托大学）；University of Oxford（牛津大学）；University of Minnesota（明尼苏达大学）
Galaxy Zoo Evo: 1 million human-annotated images of galaxies
University of Toronto（多伦多大学）；University of Nottingham（诺丁汉大学）；Open University（开放大学）；University of Edinburgh（爱丁堡大学）；University of Minnesota（明尼苏达大学）；European Space Agency（欧洲空间局）；University of Portsmouth（朴茨茅斯大学）；University of Oxford（牛津大学）；Haverford College（哈弗福德学院）；University of Manchester（曼彻斯特大学）；Università degli Studi di Milano（米兰大学）；University of Lancaster（兰开斯特大学）；Amazon（亚马逊）
Intrinsic Dimension Estimation for Radio Galaxy Zoo using Diffusion Models
Department of Computer Science（计算机科学系）；The University of Manchester（曼彻斯特大学）；Department of Physics and Astronomy（物理与天文学系）；The Alan Turing Institute（艾伦·图灵研究所）
Galaxy Zoo: Cosmic Dawn -- morphological classifications for over 41,000 galaxies in the Euclid Deep Field North from the Hawaii Two-0 Cosmic Dawn survey
The Open University（开放大学）；University of Toronto（多伦多大学）；University of Minnesota（明尼苏达大学）；European Space Agency (ESA)（欧洲航天局）；Haverford College（哈弗福德学院）；Lancaster University（兰卡斯特大学）；University of Oxford（牛津大学）
Radio Galaxy Zoo: Morphological classification by Fanaroff-Riley designation using self-supervised pre-training
University of Manchester（曼彻斯特大学）；Alan Turing Institute（艾伦·图灵研究所）；University of Oxford（牛津大学）；Macquarie University（麦考瑞大学）；University of Tasmania（塔斯马尼亚大学）；CSIRO Space & Astronomy（澳大利亚联邦科学与工业研究组织空间与天文部）；ICRAR, University of Western Australia（西澳大利亚大学星际计算与实时系统研究中心）
From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaption
Shanghai Astronomical Observatory, Chinese Academy of Sciences（中国科学院上海天文台）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）；Shanghai Key Lab for Astrophysics, Shanghai Normal University（上海师范大学上海市天体物理重点实验室）；Centre for Astrophysics Research, University of Hertfordshire（赫特福德大学天体物理研究中心）
