Astronomical archives and their importance for knowledge transfer in Basic Space Science

We summarize the nature of modern astronomical archives with special reference to spectroscopic data archives and illustrate in which ways archives can play a role in the accelerated development of Basic Space Sciences in the developing countries. Some of the conflicting issues in the current planning of archives are addressed. Astronomical spectroscopic archives constitute a tool to progress towards an intellectual environment which is required to stimulate the sociological evolution which can support the peaceful and harmonious development of states. This is especially true under the conditions generated by the rapid evolution of technology in the industrialized countries.     

Detectors For Space Science

The design of detector systems for flight applications requires the consideration of a number of issues unique to space instrumentation. Flight detectors must endure hostile radiation environments and thermal extremes. Paramount importance is given to reliability since inflight replacement is at best difficult and usually impossible. Flight detectors are also significant cost and design drivers since they often determine key requirements for flight instruments such as volume, mass, power consumption, heat dissipation and communications budgets. In this paper we describe the primary concerns in developing flight detector systems, and review the challenges posed by future NASA and ESA space science missions for detector development.     

空间VLBI的研究现状

评述了过去近三十年中空间VLBI的发展．其内容包括空间VLBI的简短发展历史,空间VLBI与地面VLBI的不同、空间VLBI的目前状况和空间VLBI的未来展望。着重介绍了作为首次空间VLBI的VSOP的各个方面情况,并给出了VSOP的连续谱、谱线普查样本,供参考。     

基于光变曲线的空间碎片基本形状分类

将未编目的空间碎片正确分类是空间态势感知的重要组成部分. 基于光变曲线, 通过仿真和实测实验, 探讨了空间碎片基本类型的机器学习分类方法. 在数据集中的仿真光变来自形状或材料不同的4类碎片, 实测光变从Mini-Mega TORTORA (MMT)数据库中提取, 实验以深度神经网络作为分类模型, 并和其他机器学习方法进行了比较. 结果显示深度卷积网络优于其他算法, 在仿真实验中对不同材料的圆柱体都能准确识别, 对其余两类卫星的识别率在90%左右; 实测实验中对火箭体和失效卫星的2分类准确率超过99%, 然而在进一步的型号/平台分类中, 准确率有所降低.     

The Basic Shape Classification of Space Debris with Light Curves

We study the machine learning method for classifying the basic shape of space debris in both simulated and observed data experiments, where light curves are used as the input features. In the dataset for training and testing, simulated light curves are derived from four types of debris within different shapes and materials. Observed light curves are extracted from Mini-Mega TORTORA (MMT) database which is a publicly accessible source of space object photometric records. The experiments employ the deep convolutional neural network, make comparisons with other machine learning algorithms, and the results show CNN (Convolutional Neural Network) is better. In simulational experiments, both types of cylinder can be distinguished perfectly, and two other types of satellite have around 90% probability to be classified. Rockets and defunct satellites can achieve 99% success rate in binary classification, but in further sub-classes classifications, the rate becomes relatively lower.     

A World-Wide Net of Solar Radio Spectrometers: e-CALLISTO

Radio spectrometers of the CALLISTO type to observe solar flares have been distributed to nine locations around the globe. The instruments observe automatically, their data is collected every day via internet and stored in a central data base. A public web-interface exists through which data can be browsed and retrieved. The nine instruments form a network called e-CALLISTO. It is still growing in the number of stations, as redundancy is desirable for full 24 h coverage of the solar radio emission in the meter and low decimeter band. The e-CALLISTO system has already proven to be a valuable new tool for monitoring solar activity and for space weather research.     

空间科学虚拟观测台体系结构研究

科学观测数据的海量增长,使得旨在实现资源互操作的虚拟观测台(VO)应运而生,并迅速受到相关学科的支持和重视。国际虚拟天文台联盟(IVOA)、行星科学数据系统(PDS)和空间物理归档检索抽取标准(SPASE)等数据组织,正加快制定数据管理模型,推动VO的发展和跨学科应用。结合空间科学应用研究的特点,在借鉴国内外天文和空间科学VO研究成果的基础上,提出了一种针对空间科学数据的Hybrid型VO体系结构,并基于此体系结构发展了一个支持同步收割、格式化管道和事件关联发现的空间科学VO原型。实践和应用结果表明,基于这种体系结构建立的VO在解决全局资源的实时同步、异构应用的高度集成和面向科学问题的数据发现等问题上,有一定的普适性。     

Utilization of the European Hubble Space Telescope Science Data archive

The Hubble Space Telescope (HST) is a very complex astronomical observing facility, producing data with unique calibration and analysis requirements. The data are collected in Science Data Archives which are available through computer networks world wide. This paper describes the approaches and technologies which are being used at the Space Telescope European Coordinating Facility (ST-ECF) to enable users in different institutions to efficiently work with HST data.