Modern surveys have provided the astronomical community with a flood of high-dimensional data, but analyses of these data often occur after their projection to lower-dimensional spaces. In this work, we introduce a local two-sample hypothesis test framework that an analyst may directly apply to data in their native space. In this framework, the analyst defines two classes based on a response variable of interest (e.g. higher-mass galaxies versus lower-mass galaxies) and determines at arbitrary points in predictor space whether the local proportions of objects that belong to the two classes significantly differs from the global proportion.
Our framework has a potential myriad of uses throughout astronomy; here, we demonstrate its efficacy by applying it to a sample of 2487 i-band-selected galaxies observed by the HST ACS in four of the CANDELS program fields. For each galaxy, we have seven morphological summary statistics along with an estimated stellar mass and star-formation rate. We perform two studies: one in which we determine regions of the seven-dimensional space of morphological statistics where high-mass galaxies are significantly more numerous than low-mass galaxies, and vice-versa, and another study where we use SFR in place of mass. We find that we are able to identify such regions, and show how high-mass/low-SFR regions are associated with concentrated and undisturbed galaxies while galaxies in low-mass/high-SFR regions appear more extended and/or disturbed than their high-mass/low-SFR counterparts.
National Mapping and Testing of Astronomical Sites in Ethiopia (NMTASE)
North-West University（西北大学）；Space Science and Geo-Spatial Institute（空间科学与地理空间研究所）；National Institute for Theoretical and Computational Sciences（国家理论与计算科学研究所）；Ethio Telecom（埃塞俄比亚电信）
Shigatse Astronomical Site Testing. I. Cloud-cover Climatology and Selected Local Meteorological Conditions
Shanghai Astronomical Observatory, Chinese Academy of Sciences（中国科学院上海天文台）；College of Computer Science, Beijing University of Technology（北京工业大学计算机学院）；Beijing Artificial Intelligence Institute, Beijing University of Technology（北京工业大学北京人工智能研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Xizang Autonomous Region Meteorological Information Network Center（西藏自治区气象信息中心）；Xizang University（西藏大学）
Characterization and on-sky testing of photonic, AWG-based astronomical spectrographs
Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange（蔚蓝海岸大学，蔚蓝海岸天文台，法国国家科学研究中心，拉格朗日实验室）；California Institute of Technology（加州理工学院）；University of California, Los Angeles (UCLA)（加州大学洛杉矶分校）；Université Grenoble Alpes, CNRS, IPAG（格勒诺布尔阿尔卑斯大学，法国国家科学研究中心，天体物理学与行星学研究所）；Aix Marseille Université, CNRS, LAM (Laboratoire d’Astrophysique de Marseille) UMR 7326（艾克斯-马赛大学，法国国家科学研究中心，马赛天体物理实验室）
MaxiMask and MaxiTrack: two new tools for identifying contaminants in astronomical images using convolutional neural networks
在这项工作中，我们提出了两个卷积神经网络分类器检测天文图像中的污染物。经过训练后，我们的分类器能够识别各种污染物，如宇宙射线、热像素和坏像素、持久性效应、卫星或飞机轨迹、残留条纹图案、模糊特征、饱和像素、衍射尖峰和图像中的跟踪错误。它们涵盖了广泛的环境条件，如视宁度、图像采样、探测器类型、光学和恒星密度。第一个分类器MaxiM ask基于每个像素属于给定污染物类的概率，执行语义分割并为每个污染物生成坏像素图。第二个分类器MaxiTrack通过计算焦平面受跟踪误差影响的概率，对整个图像和马赛克进行分类。我们从各种现代电荷耦合器件和近红外相机的真实数据中收集了训练和测试数据，这些数据通过图像模拟进行了增强。我们量化了这两个分类器的性能，并表明M axi M ask在识别宇宙射线命中方面达到了最先进的性能。由于内置的贝叶斯更新机制，这两个分类器都可以进行调整，以满足各种观测环境中的特定科学目标。
BGRem: A background noise remover for astronomical images based on a diffusion model
Radboud University Nijmegen（奈梅亨拉德堡德大学）；Nikhef（荷兰国家核子与粒子物理研究所）；University of Oxford（牛津大学）；University of Nova Gorica（新戈里察大学）；Instituto de Física Corpuscular (IFIC), CSIC-UV（瓦伦西亚-西班牙国家科学研究委员会粒子物理研究所）；University of Cape Town（开普敦大学）；South African Astronomical Observatory（南非天文台）
Identifying outliers in astronomical images with unsupervised machine learning
Nanjing University of Posts and Telecommunications（南京邮电大学）；National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；University of Chinese Academy of Sciences（中国科学院大学）
Kiwi Advanced Research and Education Network（KAREN）已被用于在新西兰Warkworth的AUT射电天文台与我们合作和进行观测的国际组织之间传输大量射电天文数据。在这里，我们报告了连接的当前状态和测试不同数据传输协议的结果。我们研究了新的UDP协议，如“海啸”和UDT，并证明了UDT协议比“海啸”和FTP更有效。我们报告我们的初步步骤，实时eVLBI和尝试直接流数据从射电望远镜接收系统的相关中心没有中间缓冲/记录。
Survey Footprint Explorer: A Browser-Based Interactive Tool for Visualizing and Cross-Matching Astronomical Survey Footprints
Waterloo Centre for Astrophysics, University of Waterloo（滑铁卢天体物理中心，滑铁卢大学）；Department of Physics and Astronomy, University of Waterloo（滑铁卢大学物理与天文系）；Center for Astronomy, Space Science and Astrophysics, Independent University, Bangladesh（孟加拉独立大学天文学、空间科学与天体物理中心）；Kapteyn Astronomical Institute, University of Groningen（格罗宁根大学卡普坦天体物理研究所）；Definity Insurance Company（Definity保险公司）
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）
