现代望远镜生成的数据集越来越大且多样化，通常包含复杂的形态结构。为了高效探索此类数据，需要能够提取和组织物理意义信息的自动化方法，理想上无需大量手动交互。本文旨在提供一个用户友好的自监督机器学习框架实现，用于探索大规模数据集的形态属性，基于BYOL（Bootstrap Your Own Latents）方法。通过生成有意义的图像嵌入而不依赖手动标注数据，该框架可支持聚类、异常检测和基于相似性的探索。与现有BYOL实现不同，astromorph支持不同维度和分辨率的数据，包括单通道FITS图像和多通道光谱立方体。该软件包以易用性为目标，提供流线型的流程脚本以简化使用，同时通过PyTorch类提供更深入的自定义选项。为了展示astromorph的实用性，我们将其应用于两个代表不同天文领域的科学案例：ALMA观测的原行星盘图像和Spitzer和Herschel观测的红外暗云。在两种情况下，我们展示了astromorph如何生成具有科学意义的嵌入，捕捉大规模样本中的形态差异和相似性。astromorph使用户能够应用一种稳健、无标注的方法来揭示天文数据集中的形态模式。在两个明显不同的数据集上的成功应用表明，该流程在广泛的成像丰富的天文背景下具有广泛的应用性，提供了一个用户友好的工具，推动观测天文学的发展。
英文摘要：
Modern telescopes generate increasingly large and diverse datasets, often consisting of complex and morphologically rich structures. To efficiently explore such data requires automated methods that can extract and organize physically meaningful information, ideally without the need for extensive manual interaction. We aim to provide a user-friendly implementation of a self-supervised machine learning framework to explore morphological properties of large datasets, based on the BYOL (Bootstrap Your Own Latents) method. By enabling the generation of meaningful image embeddings without manually labelled data, the framework will enable key tasks such as clustering, anomaly detection, and similarity based exploration. In contrast to existing BYOL implementations, astromorph accommodates data of varying dimensions and resolutions, including both single-channel FITS images and multi-channel spectral cubes. The package is built with usability in mind, offering streamlined pipeline scripts for ease of use as well as deeper customization options via PyTorch-based classes. To demonstrate the utility of astromorph, we apply it in two contrasting science cases representing different astronomical domains: images of protoplanetary disks observed with ALMA, and infrared dark clouds observed with Spitzer and Herschel. In both cases, we demonstrate how astromorph produces scientifically meaningful embeddings that capture morphological differences and similarities across large samples. astromorph enables users to apply a robust, label-free approach for uncovering morphological patterns in astronomical datasets. The successful application to two markedly different datasets suggest that the pipeline is broadly applicable across a wide range of imaging-rich astronomical context, providing a user friendly tool for advancing discovery in observational astronomy.
Fast and accurate astronomical source deblending with Density-Peak Clustering
INAF-Osservatorio Astronomico di Trieste（的里雅斯特天文台）；Dipartimento di Matematica, Sezione di Matematica e Geoscienze, Università di Trieste（的里雅斯特大学数学与地球科学系）
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）
A family portrait of disk inner rims around Herbig Ae/Be stars: Hunting for warps, rings, self shadowing, and misalignments in the inner astronomical units
SIFARI: Self-Supervised Interferometric Fitting for Astronomical Radio Imaging
Rice University（莱斯大学）；Rice Space Institute（莱斯空间研究所）；National Radio Astronomy Observatory（美国国家射电天文台）；NSF-Simons AI Institute for Cosmic Origins（NSF-西蒙斯宇宙起源人工智能研究所）；University of Pennsylvania（宾夕法尼亚大学）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
Multi-Method Machine Learning Classification of Astronomical Sources with Rubin Data Previews: a Spectroscopy-Free Approach with DP1 and DP2
SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；Kavli Institute for Particle Astrophysics and Cosmology, Stanford University（斯坦福大学卡弗里粒子天体物理与宇宙学研究所）；Instituto de Estudios Astrofísicos, Facultad de Ingeniería y Ciencias, Universidad Diego Portales（迭戈波特莱斯大学工程与科学学院天体物理研究所）；Department of Astrophysical Sciences, Princeton University（普林斯顿大学天体科学系）；NSF National Optical-Infrared Astronomy Research Laboratory（美国国家光学-红外天文研究实验室）；NSF–DOE Vera C. Rubin Observatory/NSF NOIRLab（美国国家科学基金会-能源部维拉·C·鲁宾天文台/美国国家科学基金会NOIRLab）
Revisiting the Optical F-corona from the Artemis II Total Solar Eclipse with RAW Data: How Far Can Consumer JPEG Images Be Trusted for Astronomical Photometry?
Frontier Research Institute for Interdisciplinary Sciences, Tohoku University（东北大学前沿跨学科研究院）；Department of Natural Sciences, Faculty of Science and Engineering, Tokyo City University（东京都立大学理工学部自然科学科）；National Astronomical Observatory of Japan（日本国立天文台）；Ishigakijima Astronomical Observatory（石垣岛天文台）
Image Restoration with Point Spread Function Regularization and Active Learning
College of Electronic Information and Optical Engineering, Taiyuan（太原电子信息与光学工程学院）；Peng Cheng Lab, Shenzhen（鹏城实验室）；Department of Physics, Durham University（杜伦大学物理系）；National Astronomical Observatories, Beijing（中国科学院国家天文台）；Yunnan Observatories, Kunming（中国科学院云南天文台）
A Versatile Framework for Analyzing Galaxy Image Data by Implanting Human-in-the-loop on a Large Vision Model
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；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）；College of Electronic Information and Optical Engineering, Taiyuan University of Technology（太原理工大学电子信息与光学工程学院）；School of Computer Science, South China Normal University（华南师范大学计算机学院）
