基于空时显著性的日冕物质抛射检测

日冕物质抛射(Coronal Mass Ejection, CME)是一种剧烈的太阳爆发现象, 它会对行星际空间造成严重扰动, 进而影响人类生产、生活. 基于CME的时空显著性, 将显著性检测方法引入到CME检测中, 利用结构化矩阵分解SOHO (Solar and Heliospheric Observatory)的大角度光谱日冕仪(Large Angle and Spectrometric Coronagraph Experiment, LASCO) C2的日冕图像对应的特征矩阵, 从中恢复出稀疏部分获得显著前景. 然后考虑CME运动时产生的时间显著性, 从而去除非CME结构(如冕流), 得到最终检测结果. 实验表明, 以人工目录协调数据分析中心(Coordinated Data Analysis Workshop, CDAW)检测结果为基准时, 所提方法不仅在检测CME数量上比计算机辅助跟踪软件包(Computer Aided CME Tracking Software package, CACTus)和太阳爆发事件检测系统(Solar Eruptive Event Detection System, SEEDS)有优势, 还在CME中心角度和张角宽度等特征物理参数测量上比CACTus和SEEDS更接近CDAW目录参考值.     

基于Faster R-CNN的日冕物质抛射检测方法

日冕物质抛射(Coronal Mass Ejection,CME)是一种强烈的太阳爆发现象,对空间天气和人类生活有巨大的影响,因此,日冕物质抛射检测对预报日冕物质抛射、保障人类的生产生活安全具有重要意义。现有的日冕物质抛射检测多采用人为定义特征和界定阈值等方法。由于人为定义特征不能准确表征日冕物质抛射且具有普适性的阈值难于选择,现有的方法对日冕物质抛射的检测效果有待提高。提出一种基于Faster R-CNN(Faster Region-based Convolutional Neural Networks)的日冕物质抛射检测算法。该方法首先结合CDAW(Coordinated Data Analysis Workshop Data Center),SEEDS(Solar Eruptive Even Detection System)和CACTus(Computer Aoded CME Tracking software package)3个著名的日冕物质抛射目录信息,人工标注了包含9113幅日冕图像的数据集,然后根据日冕物质抛射的图像特征较自然图像少、目标尺寸与自然图像有差异等特点,在特征提取和锚点选择方面对Faster R-CNN进行改进。以2007年6月的日冕物质抛射标注数据为测试集,本文算法检出了全部22个强日冕物质抛射事件和151个弱日冕物质抛射事件中的138个,对日冕物质抛射事件的中心角和角宽度等特征参数的检测误差分别在5°和10°以内。     

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.     

STEREO卫星的CME观测研究进展

简要回顾利用"日地关系天文台"(Solar Terrestrial Relations Observatory,STEREO)卫星的立体观测资料在日冕物质抛射(Coronal Mass Ejection,CME)研究方面已取得的一些重要进展,主要包括(1)通过极紫外成像仪观测到的日冕极紫外暗化来更准确地估计CME质量,研究CME演化的结构特征;(2)利用STEREO卫星日冕仪的双角度观测,在CME立体传播特征方面取得的新进展;(3)STEREO卫星日球成像仪具有广阔的视场范围,可以跟踪研究CME从太阳表面爆发到形成行星际日冕物质抛射(Interplanetary CME,ICME),及其在内日球层和近地空间的演化特征以及运动特征等。同时,也介绍了利用三角测量技术测定CME特征物理量的新方法。     

对一个太阳风暴及其行星际和地磁效应的研究

对一个爆发于2014年1月7日的太阳风暴进行了研究,通过对太阳活动的多波段遥感观测—来自于太阳动力学天文台(Solar Dynamics Observatory,SDO)以及太阳和日球天文台(Solar and Heliospheric Observatory,SOHO),分析了耀斑和日冕物质抛射(coronal mass ejection,CME)的爆发过程.通过地球同步轨道环境业务卫星(Geostationary Operational Environmental Satellites,GOES)对高能质子以及日地L1点的元素高级成分探测器(Advanced Composition Explorer,ACE)对当地等离子体环境的就位观测,分析了伴随太阳风暴的太阳高能粒子(solar energetic particle,SEP)事件和行星际CME(ICME)及其驱动的激波.通过地面磁场数据分析了该太阳风暴对地磁场的影响.研究结果表明:(1)耀斑脉冲相的开始时刻和CME在日面上的抛射在时序上一致.(2)高能质子主要源于CME驱动的激波加速,并非源于耀斑磁重联过程.质子的释放发生在CME传播到7.7个太阳半径的高度的时刻.(3)穿过近地空间的行星际激波鞘层的厚度和ICME本身的厚度分别为0.22 au和0.26 au.(4)行星际激波和ICME引起了多次地磁亚暴和极光,但没有产生明显的地磁暴.原因在于ICME没有包含一个规则的磁云结构或明显的南向磁场分量.     

日冕物质抛射及其驱动激波的多波段和多视角研究

<正>日冕物质抛射(Coronal Mass Ejection, CME)是太阳大气中剧烈的爆发现象之一.其爆发通常能释放大量的能量并抛射大量磁化等离子体. CME所驱动的激波能进一步导致太阳高能粒子事件(Solar Energetic Particle,SEP)的发生,并可能影响航天器和宇航员的安全.因此,研究CME及其驱动激波的形成机制和性质有利于我们更加清晰地了解及监测它们的运动过程,     

由对地全晕状日冕物质抛射诱发的地磁感应电流统计研究

太阳活动会引起输变电系统异常,特别是对超长距离输变电系统的危害尤其明显.根据SOHO/LASCO (Solar and Heliospheric Observatory/Large Angle and Spectrometric Coronagraph)的日冕物质抛射(Coronal Mass Ejection,CME)数据、华北电力大学和芬兰气象研究所获得的地磁感应电流(Geomagnetically Induced Current,GIC)数据以及地磁暴数据,分析研究了与GIC事件有关的对地晕状CME的重要观测特征和物理性质.按照对称性将晕状CME进行分类后,发现造成GIC事件的晕状CME主要有3类:完全对称型、亮度不对称型和外形不对称型.不同类型的全晕状CME驱动的GIC事件在强度、持续时间等方面特征各不相同.其中,亮度不对称型晕状CME很有可能对GIC事件影响最为严重.同时注意到GIC与地磁场随时间的变化率也具有较好的相关性.     

卫星黑子衰减触发的喷流事件

详细分析了一次太阳低层大气磁场重联触发的喷流事件.这次喷流发生在2014年8月1日,爆发自美国国家海洋和大气管理局(National Oceanic and Atmospheric Administration, NOAA)活动区12127边缘的一个卫星黑子处.该喷流爆发包括日浪、紫外喷流、极紫外高温和低温喷流.大熊湖太阳天文台(Big Bear Solar Observatory,BBSO)的Goode Solar Telescope (GST)高分辨率氧化钛(TiO)谱线的光球观测显示,喷流爆发过程中,卫星黑子一直衰减.到喷流结束,卫星黑子面积共减少了80%.在此过程中,太阳动力学天文台(Solar Dynamics Observatory, SDO)日球磁场成像仪(Helioseismic and Magnetic Imager, HMI)的视向磁场观测表明,该卫星黑子对应的负极磁场与相邻的正极磁场发生明显对消,产生喷流足部亮点.根据SDO卫星太阳大气成像仪(Atmospheric Imaging Assembly, AIA)的多波段观测,该足部亮点首先出现在紫外1600?波段.待紫外(1600?)喷流从紫外足部亮点顶部向上喷发,在极紫外波段也观测到相应的亮源.随着足点源亮度突然增强,有明显的极紫外低温喷流和日浪从足部亮点侧面喷发.从GST的高分辨率Hα图像上,可见日浪由许多精细纤维组成,这些纤维扎根在足点源的东南侧.根据从光球层过色球层再到日冕层的多波段高分辨率观测,色球中下层的磁场对消触发了这次喷流事件.向上喷发的物质流可以携带能量进入上层大气,并加热上层大气.研究结果表明,低层大气磁重联可能对解决日冕加热问题起重要作用.     

内日冕的绿线强度分布研究

日冕是太阳大气活动的关键区域,是日地空间天气的源头.受观测限制,对日冕低层大气等离子体结构和磁场状态的研究非常欠缺,国际上对于可见光波段日冕低层大气的亮度分层研究很少.利用丽江日冕仪YOGIS(Yunnan Green-line Imaging System)的日冕绿线(Fe_ⅩⅣ5303?)观测资料,对内日冕区域(1.03R_☉-1.25R_☉,R_☉表示太阳半径)亮结构及其中冕环进行了有效的强度衰减分析.对亮结构的强度在太阳径向高度上进行了指数衰减拟合,比较这些拟合结果发现所得到的静态内冕环的衰减指数在一固定值附近.然后将比较明显的冕环提取出来,通过对不同高度的绿线强度进行指数拟合,得出的衰减指数与亮结构中也比较相近,这对进一步研究日冕中的各项物理参数演化提供了参考.     

基于快速鲁棒性主成分分析的日冕喷流自动检测方法

使用快速鲁棒性主成分分析(Fast Robust Principal Component Analysis,Fast RPCA)方法对日冕序列图像中的日冕喷流活动进行检测。检测的基本思路是利用快速鲁棒性主成分分析方法中低秩和稀疏分解的思想与日冕序列图像中有着变化尺度稍小且占比较大的随机变化背景成分、变化尺度较大且占比较小的日冕喷流的特点相结合,实现随机复杂多变的动态背景和稀疏运动目标之间的分离,从而检出作为前景变化的日冕喷流。采用太阳动力学天文台(Solar Dynamics Observatory,SDO)卫星的大气成像仪(Atmospheric Imaging Assembly,AIA)两组不同时间段、不同波段、不同观测位置的日冕序列图像作为研究对象。研究内容主要包括日冕序列图像的预处理、日冕喷流检测、快速鲁棒性主成分分析方法与帧间差分法的检测结果对比分析。实验结果表明,与帧间差分法相比,快速鲁棒性主成分分析方法能够检出强度较弱的日冕喷流,且提高了日冕喷流检测的准确度。     

Automatic Detection and Tracking of Coronal Mass Ejections in Coronagraph Time Series

We present the current capabilities of a software tool to automatically detect coronal mass ejections (CMEs) based on time series of coronagraph images: the solar eruptive event detection system (SEEDS). The software developed consists of several modules: preprocessing, detection, tracking, and event cataloging. The detection algorithm is based on a 2D to 1D projection method, where CMEs are assumed to be bright regions moving radially outward as observed in a running-difference time series. The height, velocity, and acceleration of the CME are automatically determined. A threshold-segmentation technique is applied to the individual detections to automatically extract an approximate shape of the CME leading edge. We have applied this method to a 12-month period of continuous coronagraph images sequence taken at a 20-minute cadence by the Large Angle and Spectrometric Coronagraph (LASCO) instrument (using the C2 instrument only) onboard the Solar and Heliospheric Observatory (SOHO) spacecraft. Our automated method, with a high computational efficiency, successfully detected about 75% of the CMEs listed in the CDAW CME catalog, which was created by using human visual inspection. Furthermore, the tool picked up about 100% more small-size or anomalous transient coronagraph events that were ignored by human visual inspection. The output of the software is made available online at . The parameters of scientific importance extracted by the software package are the position angle, angular width, velocity, peak, and average brightness. Other parameters could easily be added if needed. The identification of CMEs is known to be somewhat subjective. As our system is further developed, we expect to make the process significantly more objective.     

Onboard Automated CME Detection Algorithm for the Visible Emission Line Coronagraph on <Emphasis Type="Italic">ADITYA-L1</Emphasis>

ADITYA-L1 is India’s first space mission to study the Sun from the Lagrange 1 position. The Visible Emission Line Coronagraph (VELC) is one of seven payloads on the ADITYA-L1 mission, which is scheduled to be launched around 2020. One of the primary objectives of the VELC is to study the dynamics of coronal mass ejections (CMEs) in the inner corona. This will be accomplished by taking high-resolution (\({\approx}\,2.51~\mbox{arcsec}\,\mbox{pixel}^{-1}\)) images of the corona from \(1.05~\mbox{R}_{\odot}\,\mbox{--}\,3~\mbox{R}_{\odot}\) at a high cadence of 1 s in the 10 Å passband centered at 5000 Å. Because telemetry at the Lagrangian 1 position is limited, we plan to implement an onboard automated CME detection algorithm. The detection algorithm is based on intensity thresholding followed by area thresholding in successive difference images that are spatially rebinned to improve the signal-to-noise ratio. We present the results of the application of this algorithm on the data from existing coronagraphs such as STEREO/SECCHI COR-1, which is a space-based coronagraph, and K-Cor, a ground-based coronagraph, because they have a field of view (FOV) that is most similar to that of VELC. Since no existing space-based coronagraph has a FOV similar to VELC, we have created synthetic coronal images for the VELC FOV after including photon noise and injected CMEs of different types. The performance of the CME detection algorithm was tested on these images. We found that for VELC images, the telemetry can be reduced by a factor of 85% or more while maintaining a CME detection rate of 70% or higher at the same time. Finally, we discuss the advantages and disadvantages of this algorithm. The application of such an onboard algorithm in future will enable us to take higher resolution images with an improved cadence from space and simultaneously reduce the load on limited telemetry. This will help understanding CMEs better by studying their characteristics with improved spatial and temporal resolution.     

Solar Sources of Interplanetary Coronal Mass Ejections During the Solar Cycle 23/24 Minimum

We examine solar sources for 20 interplanetary coronal mass ejections (ICMEs) observed in 2009 in the near-Earth solar wind. We performed a detailed analysis of coronagraph and extreme ultraviolet (EUV) observations from the Solar Terrestrial Relations Observatory (STEREO) and Solar and Heliospheric Observatory (SOHO). Our study shows that the coronagraph observations from viewpoints away from the Sun–Earth line are paramount to locate the solar sources of Earth-bound ICMEs during solar minimum. SOHO/LASCO detected only six CMEs in our sample, and only one of these CMEs was wider than 120^°. This demonstrates that observing a full or partial halo CME is not necessary to observe the ICME arrival. Although the two STEREO spacecraft had the best possible configuration for observing Earth-bound CMEs in 2009, we failed to find the associated CME for four ICMEs, and identifying the correct CME was not straightforward even for some clear ICMEs. Ten out of 16 (63 %) of the associated CMEs in our study were “stealth” CMEs, i.e. no obvious EUV on-disk activity was associated with them. Most of our stealth CMEs also lacked on-limb EUV signatures. We found that stealth CMEs generally lack the leading bright front in coronagraph images. This is in accordance with previous studies that argued that stealth CMEs form more slowly and at higher coronal altitudes than non-stealth CMEs. We suggest that at solar minimum the slow-rising CMEs do not draw enough coronal plasma around them. These CMEs are hence difficult to discern in the coronagraphic data, even when viewed close to the plane of the sky. The weak ICMEs in our study were related to both intrinsically narrow CMEs and the non-central encounters of larger CMEs. We also demonstrate that narrow CMEs (angular widths ≤?20^°) can arrive at Earth and that an unstructured CME may result in a flux rope-type ICME.     

Automatic Determination of the Conic Coronal Mass Ejection Model Parameters

Characterization of the three-dimensional structure of solar transients using incomplete plane of sky data is a difficult problem whose solutions have potential for societal benefit in terms of space weather applications. In this paper transients are characterized in three dimensions by means of conic coronal mass ejection (CME) approximation. A novel method for the automatic determination of cone model parameters from observed halo CMEs is introduced. The method uses both standard image processing techniques to extract the CME mass from white-light coronagraph images and a novel inversion routine providing the final cone parameters. A bootstrap technique is used to provide model parameter distributions. When combined with heliospheric modeling, the cone model parameter distributions will provide direct means for ensemble predictions of transient propagation in the heliosphere. An initial validation of the automatic method is carried by comparison to manually determined cone model parameters. It is shown using 14 halo CME events that there is reasonable agreement, especially between the heliocentric locations of the cones derived with the two methods. It is argued that both the heliocentric locations and the opening half-angles of the automatically determined cones may be more realistic than those obtained from the manual analysis.     

Multiscale Edge Detection in the Corona

Coronal Mass Ejections (CMEs) are challenging objects to detect using automated techniques, due to their high velocity and diffuse, irregular morphology. A necessary step to automating the detection process is to first remove the subjectivity introduced by the observer used in the current, standard, CME detection and tracking method. Here we describe and demonstrate a multiscale edge detection technique that addresses this step and could serve as one part of an automated CME detection system. This method provides a way to objectively define a CME front with associated error estimates. These fronts can then be used to extract CME morphology and kinematics. We apply this technique to a CME observed on 18 April 2000 by the Large Angle Solar COronagraph experiment (LASCO) C2/C3 and a CME observed on 21 April 2002 by LASCO C2/C3 and the Transition Region and Coronal Explorer (TRACE). For the two examples in this work, the heights determined by the standard manual method are larger than those determined with the multiscale method by ≈10% using LASCO data and ≈20% using TRACE data.     

Automatic Detection and Classification of Coronal Mass Ejections

We present an automatic algorithm to detect, characterize, and classify coronal mass ejections (CMEs) in Large Angle Spectrometric Coronagraph (LASCO) C2 and C3 images. The algorithm includes three steps: (1) production running difference images of LASCO C2 and C3; (2) characterization of properties of CMEs such as intensity, height, angular width of span, and speed, and (3) classification of strong, median, and weak CMEs on the basis of CME characterization. In this work, image enhancement, segmentation, and morphological methods are used to detect and characterize CME regions. In addition, Support Vector Machine (SVM) classifiers are incorporated with the CME properties to distinguish strong CMEs from other weak CMEs. The real-time CME detection and classification results are recorded in a database to be available to the public. Comparing the two available CME catalogs, SOHO/LASCO and CACTus CME catalogs, we have achieved accurate and fast detection of strong CMEs and most of weak CMEs.     

Automatic Detection and Extraction of Coronal Dimmings from SDO/AIA Data

The volume of data anticipated from the Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) highlights the necessity for the development of automatic-detection methods for various types of solar activity. Initially recognized in the 1970s, it is now well established that coronal dimmings are closely associated with coronal mass ejections (CMEs), and they are particularly noted as a reliable indicator of front-side (halo) CMEs, which can be difficult to detect in white-light coronagraph data. Existing work clearly demonstrates that several properties derived from the analysis of coronal dimmings can give useful information about the associated CME. The development and implementation of an automated coronal-dimming region detection and extraction algorithm removes visual observer bias, however unintentional, from the determination of physical quantities such as spatial location, area, and volume. This allows for reproducible, quantifiable results to be mined from very large data sets. The information derived may facilitate more reliable early space-weather detection, as well as offering the potential for conducting large-sample studies focused on determining the geo-effectiveness of CMEs, coupled with analysis of their associated coronal dimming signatures. In this paper we present examples of both simple and complex dimming events extracted using our algorithm, which will be run as a module for the SDO/Computer Vision Centre. Contrasting and well-studied events at both the minimum and maximum of solar cycle 23 are identified in Solar and Heliospheric Observatory/Extreme ultra-violet Imaging Telescope (SOHO/EIT) data. A more recent example extracted from Solar and Terrestrial Relations Observatory/Extreme Ultra-Violet Imager (STEREO/EUVI) data is also presented, demonstrating the potential for the anticipated application to SDO/AIA data. The detection part of our algorithm is based largely on the principle of operation of the NEMO software, namely the detection of significant variation in the statistics of the EUV image pixels (Podladchikova and Berghmans in Solar Phys. 228, 265?–?284, 2005). As well as running on historic data sets, the presented algorithm is capable of detecting and extracting coronal dimmings in near real-time.