We describe an image analysis supervised learning algorithm that can automatically classify galaxy images. The algorithm is first trained using a manually classified images of elliptical, spiral, and edge-on galaxies. A large set of image features is extracted from each image, and the most informative features are selected using Fisher scores. Test images can then be classified using a simple Weighted Nearest Neighbor rule such that the Fisher scores are used as the feature weights. Experimental results show that galaxy images from Galaxy Zoo can be classified automatically to spiral, elliptical and edge-on galaxies with accuracy of ~90% compared to classifications carried out by the author. Full compilable source code of the algorithm is available for free download, and its general-purpose nature makes it suitable for other uses that involve automatic image analysis of celestial objects.
Photometric Super-Resolution for Improving Galaxy Morphological Measurements using Conditional Generative Adversarial Networks
University of California, Santa Cruz（加州大学圣克鲁兹分校）；The Johns Hopkins University（约翰斯·霍普金斯大学）；Vall d´Hebron Institute of Oncology (VHIO)（瓦尔德赫布龙肿瘤研究所）；University of Hawaii, Hilo（夏威夷大学希洛分校）
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）
Approximate Bayesian Computation for Astronomical Model Analysis: A Case Study in Galaxy Demographics and Morphological Transformation at High Redshift
“近似贝叶斯计算”（ABC）代表了一种用于分析复杂随机系统的强大方法，对于该复杂随机系统，在任意输入参数集下观察到的数据的似然性可能是完全难以处理的-后一种情况使得基于易处理似然性的贝叶斯统计推断（例如，常规马尔可夫链蒙特卡罗模拟; MCMC）的标准机器无用。在这篇文章中，我们展示了潜在的ABC天文模型分析的应用程序在高红移星系的形态转变的案例研究。为此，首先，我们发展了一个早期宇宙中合并和长期演化竞争过程的随机模型;其次，通过与CANDELS/EGS数据集中观测到的大质量（M_gal > 10^11 M_sun）星系（在1.5 < z < 3）的人口统计数据进行基于ABC的比较，我们推导出该模型关键参数的后验概率密度。本文展示的ABC的“顺序蒙特卡罗”（SMC）实现，具有自生成的目标序列和自细化MCMC内核，是这一重要统计算法的最有效的当代方法之一。我们强调，以及通过我们所选择的案例研究的价值，仔细的汇总统计选择，并展示了两个现代战略的评估和优化在这方面。最终，我们的ABC分析高红移形态的混合回报严格的限制在早期宇宙的合并率的演变，有利于主要合并（与光盘生存或快速改造）的长期演变的机制，最负责建立在早期型磁盘的第一代凸起。
Star formation and morphological properties of galaxies in the Pan-STARRS $3 π$ survey- I. A machine learning approach to galaxy and supernova classification
