Rotation and flipping invariant self-organizing maps with astronomical images: A cookbook and application to the VLA Sky Survey QuickLook images

IF 1.9 4区 物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS Astronomy and Computing Pub Date : 2024-04-01 DOI:10.1016/j.ascom.2024.100824
A.N. Vantyghem , T.J. Galvin , B. Sebastian , C.P. O’Dea , Y.A. Gordon , M. Boyce , L. Rudnick , K. Polsterer , H. Andernach , M. Dionyssiou , P. Venkataraman , R. Norris , S.A. Baum , X.R. Wang , M. Huynh
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引用次数: 0

Abstract

Modern wide field radio surveys typically detect millions of objects. Manual determination of the morphologies is impractical for such a large number of radio sources. Techniques based on machine learning are proving to be useful for classifying large numbers of objects. The self-organizing map (SOM) is an unsupervised machine learning algorithm that projects a many-dimensional dataset onto a two- or three-dimensional lattice of neurons. This dimensionality reduction allows the user to visualize common features of the data better and develop algorithms for classifying objects that are not otherwise possible with large datasets. To this aim, we use the PINK implementation of a SOM. PINK incorporates rotation and flipping invariance so that the SOM algorithm may be applied to astronomical images. In this cookbook we provide instructions for working with PINK, including preprocessing the input images, training the model, and offering lessons learned through experimentation. The problem of imbalanced classes can be improved by careful selection of the training sample and increasing the number of neurons in the SOM (chosen by the user). Because PINK is not scale-invariant, structure can be smeared in the neurons. This can also be improved by increasing the number of neurons in the SOM.

We also introduce pyink, a Python package used to read and write PINK binary files, assist in common preprocessing operations, perform standard analyses, visualize the SOM and preprocessed images, and create image-based annotations using a graphical interface. A tutorial is also provided to guide the user through the entire process. We present an application of PINK to VLA Sky Survey (VLASS) images. We demonstrate that the PINK is generally able to group VLASS sources with similar morphology together. We use the results of PINK to estimate the probability that a given source in the VLASS QuickLook Catalogue is actually due to sidelobe contamination.

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天文图像的旋转和翻转不变自组织图:VLA 巡天 QuickLook 图像的烹饪手册与应用
现代广域射电巡天通常会探测到数百万个天体。对于如此大量的射电源,人工确定其形态是不切实际的。事实证明,基于机器学习的技术有助于对大量物体进行分类。自组织图(SOM)是一种无监督机器学习算法,可将多维数据集投射到二维或三维神经元网格上。这种降维方法能让用户更好地直观了解数据的共同特征,并开发出大量数据集无法实现的物体分类算法。为此,我们使用 PINK 实现 SOM。PINK 实现了旋转和翻转不变性,因此 SOM 算法可以应用于天文图像。在这本手册中,我们提供了使用 PINK 的说明,包括预处理输入图像、训练模型,以及通过实验总结经验教训。通过仔细选择训练样本和增加 SOM 中神经元的数量(由用户选择),可以改善类不平衡的问题。由于 PINK 并非尺度不变,因此神经元中的结构可能会模糊不清。我们还介绍了 pyink,这是一个 Python 软件包,用于读写 PINK 二进制文件、辅助常用预处理操作、执行标准分析、可视化 SOM 和预处理图像,以及使用图形界面创建基于图像的注释。我们还提供了一个教程,指导用户完成整个过程。我们介绍了 PINK 在 VLA 巡天(VLASS)图像中的应用。我们证明 PINK 通常能够将形态相似的 VLASS 来源归为一类。我们利用 PINK 的结果来估算 VLASS QuickLook Catalogue 中的某个源实际上是由于侧叶污染造成的概率。
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来源期刊
Astronomy and Computing
Astronomy and Computing ASTRONOMY & ASTROPHYSICSCOMPUTER SCIENCE,-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
4.10
自引率
8.00%
发文量
67
期刊介绍: Astronomy and Computing is a peer-reviewed journal that focuses on the broad area between astronomy, computer science and information technology. The journal aims to publish the work of scientists and (software) engineers in all aspects of astronomical computing, including the collection, analysis, reduction, visualisation, preservation and dissemination of data, and the development of astronomical software and simulations. The journal covers applications for academic computer science techniques to astronomy, as well as novel applications of information technologies within astronomy.
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