ASO-S卫星工程LST爆发模式触发和终止方案

先进天基太阳天文台(ASO-S)是计划于2021年底或2022年上半年发射的中国首颗综合性太阳探测卫星,莱曼阿尔法太阳望远镜(LST)作为ASO-S的有效载荷之一,具体包括莱曼阿尔法全日面成像仪(SDI)、日冕仪(SCI)以及白光望远镜(WST) 3台科学仪器和2台导行镜(GT),其主要目标是在多个波段对太阳上的两类剧烈爆发现象(太阳耀斑和日冕物质抛射)进行连续不间断的高分辨率观测.为了实现这一观测目标, LST所有仪器的观测模式中均包含了一种针对爆发事件而设置的爆发模式.该模式下, SCI将以更高的频率进行图像采集, SDI和WST则以更高的频率对爆发所在区域进行图像采集.测试结果表明,观测图像经过中值滤波、像元合并处理后,可以通过监测图像各像元亮度的相对变化提取爆发事件的时间和位置信息.这些信息将为LST观测模式间的相互切换提供重要电子学输入.     

空间太阳硬X射线成像仪量能器读出电子学设计

先进天基太阳天文台(ASO-S)是中国科学院空间科学先导专项2期规划的太阳观测卫星,其针对第25个太阳活动峰年,同时观测太阳磁场、日冕物质抛射和太阳耀斑爆发.硬X射线成像仪(HXI)作为该卫星3个科学载荷之一,实现了高时间分辨率和空间分辨率的太阳硬X射线成像观测,其量能器由99套溴化镧闪烁晶体-光电倍增管探测单元和读出电子学板构成,实现了30–200 keV的硬X射线光子能谱测量.针对HXI量能器的观测需求,设计了一套空间高事例率读出电子学系统,并通过实验室测试,证明了该系统单事例读出死时间小于2μs,同时验证了该系统电子学噪声小于120 fC,积分非线性小于2%,满足HXI仪器要求.     

ASO-S/FMG稳像系统的柔性支撑摆镜设计与仿真

全日面矢量磁像仪(Full-disk vector MagnetoGraph, FMG)是先进天基太阳天文台(Advanced Space-based Solar Observatory, ASO-S)的主载荷之一,摆镜作为FMG稳像系统的重要组成部分,其力学性能是决定载荷观测指标的重要一环,需要对其进行仿真.设计了一种侧面固定的柔性支撑镜体结构,通过调整垫片厚度,可以得到由弹性压片提供的不同大小支持力.为了确定合理的支持力范围,建立3种不同参数模型,使用有限元软件对摆镜模型进行了在轨无重力环境的静力学仿真以及摆镜摆动频率为100 Hz、摆幅为±0.1 mrad时的动力学仿真,并进一步分析了地面有重力试验环境对面形的影响.仿真结果表明所设计的摆镜机构模型均满足通光口径内静态和动态的面形波峰波谷差(PeakValley, PV)小于1/10波长,面形均方根误差(Root Mean Square, RMS)小于1/40波长的光学系统要求,地面有重力环境不会对面形产生显著影响.在仿真结果的基础上,对其中一种模型进行加工装配和实际测试,测试结果表明摆镜结构设计合理,仿真计算结果有效.     

硬X射线成像仪量能器测试系统的设计与实现

硬X射线成像仪(Hard X-ray Imager, HXI)是先进天基太阳天文台(Advanced Space-based Solar Observatory, ASO-S)的3大载荷之一, 其中量能器作为其重要组成部分, 承担着观测30--200keV能段的太阳硬X射线的任务. 在卫星发射之前, 需要开展大量的测试工作, 以确保HXI量能器的各项功能和性能满足设计需求. HXI量能器通道数众多, 内含99个溴化镧探测器, 分别由8块相同的前端电子学板控制. 除了对各个通道的性能进行测试外, 地检系统还需模拟量能器在轨面对不同太阳活动时的运行情况, 对量能器进行全面完备的测试. 此外, 地检系统还需足够稳定, 能满足量能器在单机测试、环境试验、热真空与振动等多个不同测试项目的长时间测试需求. 为此, 设计了地检板与上位机软件, 结合放射源、直流电源、高压模块等组成一套HXI量能器的地检系统, 对8块前端电子学板实现同步配置与管理, 能高效完成指令发送与数据接收, 满足量能器最大数据输出带宽400Mbps的需求. 利用该系统, 在地面完成了HXI量能器的功能、性能验证, 获得了量能器的线性、死时间、能量分辨率等各项性能指标, 为HXI量能器的在轨高性能运行提供了保障.     

ASO-S卫星HXI量能器探测单元的标定

先进天基太阳天文台卫星(Advanced Space-based Solar Observatory, ASO-S)是中国科学院第2批空间科学先导专项之一,其主要目标是同时观测太阳磁场、耀斑和日冕物质抛射,并对3者之间的相互关系和内在联系进行研究.硬X射线成像仪(HXI)是ASOS卫星的3大载荷之一,它通过对太阳活动发射的硬X射线进行傅里叶调制成像,实现高空间分辨率和高时间分辨率的太阳能谱成像观测.量能器单机是HXI的关键单机之一,其主要任务是精准测量通过每对光栅后太阳硬X射线的能量和通量.主要介绍了量能器单机的工作原理及其关键指标要求、标定设备及标定方案,最后给出了标定结果,从而验证了量能器单机方案设计的合理性.     

ASO-S卫星基于太阳导行镜的高精度高稳定度姿态控制系统

先进天基太阳天文台(Advanced Space-based Solar Observatory, ASO-S)卫星姿控分系统的主要任务是实现高精度、高稳定度对日指向控制. ASO-S卫星的科学载荷中,白光望远镜(White-light Solar Telescope, WST)前端配置了太阳导行镜(Guide Telescope, GT)稳像系统,利用正交分布光电二极管组成的边缘探测器测量导行镜光轴与太阳中心的偏差角.提出了一种将GT测量值引入姿态控制闭环的控制方法:利用星敏陀螺定姿算法获得卫星-太阳方向姿态偏差, GT测量值确定非卫星-太阳方向姿态偏差;以4斜装反作用轮组为执行机构,进行三轴零动量稳定姿态控制.通过数学仿真验证,基于GT测量值的姿态控制器在非卫星-太阳方向的绝对指向精度优于2′′、相对姿态稳定度优于1′′/60 s,满足ASO-S卫星高精度高稳定度的对日指向要求.     

ASO-S/HXI太阳指向镜算法研究

硬X射线成像是研究太阳耀斑等爆发现象的重要手段.由于采用调制成像而非直接成像的原因, X射线图像在日面上的位置需要借助太阳指向镜提供的仪器指向的日面坐标来确定.因此,指向信息对于耀斑定位实现多波段研究,理解太阳耀斑的物理过程具有重要的科学意义.在此对两种太阳指向镜指向信息的获取算法进行了测试.结合太阳指向镜的设计方案,首先利用SDO (Solar Dynamics Observatory)/AIA (Atmospheric Imaging Assembly) 4500?的数据产生测试图像,其次对其进行二值化处理,分别提取日面轮廓和4个边角指定区域面积;最后分别利用最小二乘法和四象限法对太阳中心坐标进行反演.初步结果显示最小二乘法受随机噪声影响小,定位精度相对稳定约为0.25′′,并可提供四象限法解算的初值;后者的精度可以优于0.14′′,但受随机噪声影响较大.两种算法的精度都显著优于硬X射线成像仪(Hard X-ray Imager, HXI)太阳指向镜的设计要求,可为指向数据在将来科学分析中的实际应用提供参考.     

针对ASO-S/HXI光栅摆放角分布的测试与分析

先进天基太阳天文台(ASO-S)卫星的3大载荷之一硬X射线成像仪(Hard X-ray Imager, HXI)是一套基于傅立叶变换调制成像技术的望远镜.它利用91组不同摆放角和节距的光栅子准直器排列摆布,获得45个基于空间调制的傅立叶变换对,重建太阳耀斑源30–200 keV的硬X射线像,最高分辨率可达3.1′′.在光栅节距已经确定的前提下,它的摆放角分布仍会影响成像质量.通过对HXI仪器傅立叶分量μν分布与点扩散函数(PSF)的空间演化关系分析研究,寻求HXI光栅摆放角的最优分布.其结果将作为改进HXI仪器设计和开发相应科学分析软件的依据.     

基于边缘探测器的FMG稳像探测研究

全日面矢量磁像仪(Full-disk vector Magnetograph, FMG)是先进天基太阳天文台(Advanced Space-based Solar Observatory, ASO-S)卫星的主要载荷之一,为确保其可获得高空间分辨率的全日面太阳矢量磁场图, FMG配备了一套高精度稳像控制系统.FMG稳像系统采用边缘探测器对太阳入射倾斜偏差进行测量.基于FMG望远镜的太阳像仿真数据分析边缘探测器在太阳像能量分布下的输出特性以及太阳像尺寸与边缘探测器量程和灵敏度的关系.最后,在实验室搭建了一套验证系统对FMG稳像系统边缘探测器进行了相关性能验证实验.实验结果与理论分析一致,太阳像尺寸越大,边缘探测器量程越大,边缘探测器灵敏度越低.     

天文数据库回顾与展望

随着新一代巡天观测、时域观测等天文项目的推进,当前的天文数据量越来越大。面对天文领域日益增长的大数据集和大数据流,需要一些高效的数据存储和数据处理方法来对数据进行管理。天文数据库以及在其基础上提供的数据服务的出现部分解决了这样的问题。借助于计算机技术和信息技术的进步,一些更加专业的针对天文领域的数据发现、数据挖掘、数据交互等工作正在逐步标准化。基于虚拟天文台技术和天文信息学的新型天文数据服务正深入天文用户的日常科研生活中。首先简单回顾了天文数据库的历史,然后通过对几个典型天文数据库的举例分析,从天文数据库的类型、提供的服务等方面介绍了当前天文数据库的特点和最新进展,并对今后天文数据库的发展做了展望。     

Multidimensional indexing tools for the virtual observatory

The last decade has seen a dramatic change in the way astronomy is carried out. The dawn of the the new microelectronic devices, like CCDs has dramatically extended the amount of observed data. Large, in some cases all sky surveys emerged in almost all the wavelength ranges of the observable spectrum of electromagnetic waves. This large amount of data has to be organized, published electronically and a new style of data retrieval is essential to exploit all the hidden information in the multiwavelength data. Many statistical algorithms required for these tasks run reasonably fast when using small sets of in‐memory data, but take noticeable performance hits when operating on large databases that do not fit into memory. We utilize new software technologies to develop and evaluate fast multidimensional indexing schemes that inherently follow the underlying, highly non‐uniform distribution of the data: they are layered uniform indices, hierarchical binary space partitioning, and sampled flat Voronoi tessellation of the data. These techniques can dramatically speed up operations such as finding similar objects by example, classifying objects or comparing extensive simulation sets with observations. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

A machine learning classification broker for the LSST transient database

We describe the largest data‐producing astronomy project in the coming decade – the LSST (Large Synoptic Survey Telescope). The enormous data output, database contents, knowledge discovery, and community science expected from this project will impose massive data challenges on the astronomical research community. One of these challenge areas is the rapid machine learning, data mining, and classification of all novel astronomical events from each 3‐gigapixel (6‐GB) image obtained every 20 seconds throughout every night for the project duration of 10 years.We describe these challenges and a particular implementation of a classification broker for this data fire hose. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

VOFilter： Bridging Virtual Observatory and Industrial Office Applications

VOFilter is an XML based filter developed by the Chinese Virtual Observatory project to transform tabular data files from VOTable format into OpenDocument format. VOTable is an XML format defined for the exchange of tabular data in the context of the Virtual Observatory (VO). It is the first Proposed Recommendation defined by International Virtual Observatory Alliance, and has obtained wide support from both the VO community and many Astronomy projects. OpenOffice.org is a mature, open source, and front office application suite with the advantage of native support of industrial standard OpenDocument XML file format. Using the VOFilter, VOTable files can be loaded in OpenOffice.org Calc, a spreadsheet application, and then displayed and analyzed as other spreadsheet files. Here, the VOFilter acts as a connector, bridging the coming VO with current industrial office applications. We introduce Virtual Observatory and technical background of the VOFilter. Its workflow, installation and usage are presented. Existing problems and limitations are also discussed together with the future development plans.     

Distributed intelligence in an astronomical Distributed Sensor Network

The Telescope Alert Operations Network System (TALONS) was designed and developed in the year 2000, around the architectural principles of a distributed sensor network. This network supported the original Rapid Telescopes for Optical Response (RAPTOR) project goals; however, only with further development could TALONS meet the goals of the larger Thinking Telescope Project. The complex objectives of the Thinking Telescope project required a paradigm shift in the software architecture – the centralised intelligence merged into the TALONS network operations could no longer meet all of the new requirements. The intelligence needed to be divorced from the network operations and developed as a series of peripheral intelligent agents, distributing the decision making and analytical processes based on the temporal volatility of the data. This paper is presented as only one part of the poster from the workshop and in it we will explore the details of this architecture and how that merges with the current Thinking Telescope system to meet our project goals. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Automated probabilistic classification of transients and variables

There is an increasing number of large, digital, synoptic sky surveys, in which repeated observations are obtained over large areas of the sky in multiple epochs. Likewise, there is a growth in the number of (often automated or robotic) follow‐up facilities with varied capabilities in terms of instruments, depth, cadence, wavelengths, etc., most of which are geared toward some specific astrophysical phenomenon. As the number of detected transient events grows, an automated, probabilistic classification of the detected variables and transients becomes increasingly important, so that an optimal use can be made of follow‐up facilities, without unnecessary duplication of effort. We describe a methodology now under development for a prototype event classification system; it involves Bayesian and Machine Learning classifiers, automated incorporation of feedback from follow‐up observations, and discriminated or directed follow‐up requests. This type of methodology may be essential for the massive synoptic sky surveys in the future. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

A Comparison of BBN,ADTree and MLP in separating Quasars from Large Survey Catalogues

We compare the performance of Bayesian Belief Networks (BBN), Multilayer Perception (MLP) networks and Alternating Decision Trees (ADtree) on separating quasars from stars with the database from the 2MASS and FIRST survey catalogs. Having a train- ing sample of sources of known object types, the classifiers are trained to separate quasars from stars. By the statistical properties of the sample, the features important for classifica- tion are selected. We compare the classification results with and without feature selection. Experiments show that the results with feature selection are better than those without feature selection. From the high accuracy found, it is concluded that these automated methods are robust and effective for classifying point sources. They may all be applied to large survey projects (e.g. selecting input catalogs) and for other astronomical issues, such as the parame- ter measurement of stars and the redshift estimation of galaxies and quasars.