Spiral-Elliptical automated galaxy morphology classification from telescope images

IF 1.9 4区 物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS
M.J. Baumstark, G. Vinci
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引用次数: 0

Abstract

The classification of galaxy morphologies is an important step in the investigation of theories of hierarchical structure formation. While human expert visual classification remains quite effective and accurate, it cannot keep up with the massive influx of data from emerging sky surveys. A variety of approaches have been proposed to classify large numbers of galaxies; these approaches include crowdsourced visual classification, and automated and computational methods, such as machine learning methods based on designed morphology statistics and deep learning. In this work, we develop two novel galaxy morphology statistics, descent average and descent variance, which can be efficiently extracted from telescope galaxy images. We further propose simplified versions of the existing image statistics concentration, asymmetry, and clumpiness, which have been widely used in the literature of galaxy morphologies. We utilize the galaxy image data from the Sloan Digital Sky Survey to demonstrate the effective performance of our proposed image statistics at accurately detecting spiral and elliptical galaxies when used as features of a random forest classifier.

基于望远镜图像的螺旋椭圆星系形态自动分类
星系形态的分类是研究分层结构形成理论的重要步骤。虽然人类专家的视觉分类仍然相当有效和准确,但它无法跟上新兴天空调查数据的大量涌入。人们提出了各种各样的方法来对大量的星系进行分类;这些方法包括众包视觉分类,自动化和计算方法,如基于设计形态学统计和深度学习的机器学习方法。本文提出了两种新的星系形态统计方法,即下降平均值和下降方差,可以有效地从望远镜星系图像中提取。我们进一步提出了在星系形态学文献中广泛使用的现有图像统计的集中、不对称和团块的简化版本。我们利用来自斯隆数字巡天的星系图像数据来证明我们提出的图像统计在作为随机森林分类器的特征时准确检测螺旋星系和椭圆星系的有效性能。
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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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