New generation radio interferometers encode signals from thousands of antenna feeds across large bandwidth. Channelizing and correlating this data requires networking capabilities that can handle unprecedented data rates with reasonable cost. The Canadian Hydrogen Intensity Mapping Experiment (CHIME) correlator processes 8-bits from N=2048 digitizer inputs across 400~MHz of bandwidth. Measured in $N^2~\times $ bandwidth, it is the largest radio correlator that has been built. Its digital back-end must exchange and reorganize the 6.6~terabit/s produced by its 128 digitizing and channelizing nodes, and feed it to the 256-node spatial correlator in a way that each node obtains data from all digitizer inputs but across a small fraction of the bandwidth (i.e. `corner-turn'). In order to maximize performance and reliability of the corner-turn system while minimizing cost, a custom networking solution has been implemented. The system makes use of Field Programmable Gate Array (FPGA) transceivers to implement direct, passive, full-mesh, high speed serial connections between sixteen circuit boards in a crate, to exchange data between crates, and to offload the data to a cluster of 256 graphics processing unit (GPU) nodes using standard 10~Gbit/s Ethernet links. The GPU nodes complete the corner-turn by combining data from all crates and then computing visibilities. Eye diagrams and frame error counters confirm error-free operation of the corner-turn network in both the currently operating CHIME Pathfinder telescope (a prototype for the full CHIME telescope) and a representative fraction of the full CHIME hardware providing an end-to-end system validation.
An analysis of an equivalent corner-turn system built with Ethernet switches instead of custom passive data links is provided.
Machine learning classification of baseband data of CHIME FRBs
National Chung Hsing University（中兴大学）；National Tsing Hua University（清华大学）；National Centre for Nuclear Research（国家核研究中心）；Universiti Malaya（马来亚大学）；The Australian National University（澳大利亚国立大学）；National Centre for Radio Astrophysics (NCRA–TIFR)（国家射电天体物理学中心（NCRA-TIFR））
Innovative Web Tool for Remote Data Acquisition and Analysis: Customized for SKA Low frequency Beamforming Test Bed LPDA Array at Gauribidanur Radio Observatory
Cobalt: A GPU-based correlator and beamformer for LOFAR
对于低频射电天文学，通用硬件上的软件相关和波束形成是定制设计硬件的可行替代方案。LOFAR是新一代射电望远镜，中心位于荷兰，在德国，法国，爱尔兰，拉脱维亚，波兰，瑞典和英国设有国际站，自2004年以来，它已成功使用基于IBM Blue Gene技术的软件实时处理器。从那时起，技术的发展使我们能够建立一个基于商业现成组件的系统，该系统将相同的功能与较低的运营成本相结合。在本文中，我们描述了一个基于GPU的关联函数和波束形成器的设计和实现具有相同的能力，蓝色基因为基础的系统。我们专注于所采取的设计方法，并显示在选择一个合适的系统所面临的挑战。软件系统的设计、实现和验证充分体现了现代测试驱动开发方法的价值。基于三年运行的运行经验表明，通用系统是以前基于超级计算机的系统或定制设计的硬件的良好替代品。
Deep learning of multi-element abundances from high-resolution spectroscopic data
人工神经网络的深度学习越来越受到关注，因为它在数据驱动的天文学方面具有潜力。然而，这种方法通常不提供不确定性，并且不处理训练数据中的不完整性和噪声。在这项工作中，我们使用APOGEE数据设计了一个用于高分辨率光谱分析的神经网络，该数据模仿标准光谱分析的方法：使用全波长范围确定恒星参数，但单个元素丰度使用光谱的审查部分。我们用一个定制的目标函数来训练这个网络，该目标函数处理不完整和有噪声的训练数据，并应用辍学变分推理来推导我们预测的不确定性。我们确定的参数和丰度为18个单独的元素在$\approx 0.03$ dex水平，即使在低信噪比。我们证明了我们的方法返回的不确定性是对精度的现实估计，当遇到训练集之外的输入或输出时，它们会自动爆炸，从而保护用户免受不必要的外推。通过使用标准的深度学习工具进行GPU加速，我们的方法非常快，允许在单个低成本GPU上在10分钟内分析整个$\approx250,000$光谱的APOGEE数据集。我们发布了整个APOGEE DR 14数据集的恒星参数和18个具有相关不确定性的单个元素丰度。与此同时，我们发布了astroNN，这是一个经过良好测试的开源Python软件包，它是为这项工作开发的，但它也被设计为天文学深度学习的通用软件包。astroNN可在https://github.com/henrysky/astroNN上获得http://astroNN.…
iDaVIE v1.0: A virtual reality tool for interactive analysis of astronomical data cubes
Inter-University Institute for Data Intensive Astronomy, University of Cape Town（开普敦大学数据密集型天文学跨大学研究所）；Department of Astronomy, University of Cape Town（开普敦大学天文系）；Armagh Observatory and Planetarium（阿马赫天文台和天文馆）；INAF - Istituto di Radioastronomia（意大利国家天体物理研究所射电天文研究所）；Astrophysical Observatory of Catania, National Institute for Astrophysics（卡塔尼亚天体物理观测站，意大利国家天体物理研究所）；Kapteyn Astronomical Institute, University of Groningen（格罗宁根大学卡普坦天文研究所）
