Automated Detection of Solar Radio Bursts Using a Statistical Method

Solar Physics - In early September 2017, a series of solar flares and coronal mass ejections (CMEs) erupted from the Sun. The Cor2a coronagraph, a unit of the Sun Earth Connection Coronal and...     

A Comparative Evaluation of Automated Solar Filament Detection

We present a comparative evaluation for automated filament detection in Hα solar images. By using metadata produced by the Advanced Automated Filament Detection and Characterization Code (AAFDCC) module, we adapted our trainable feature recognition (TFR) module to accurately detect regions in solar images containing filaments. We first analyze the AAFDCC module’s metadata and then transform it into labeled datasets for machine-learning classification. Visualizations of data transformations and classification results are presented and accompanied by statistical findings. Our results confirm the reliable event reporting of the AAFDCC module and establishes our TFR module’s ability to effectively detect solar filaments in Hα solar images.     

Automatic Solar Flare Detection Using Neural Network Techniques

We present a new method for automatic detection of flare events from images in the optical range. The method uses neural networks for pattern recognition and is conceived to be applied to full-disk Himages. Images are analyzed in real time, which allows for the design of automatic patrol processes able to detect and record flare events with the best time resolution available without human assistance. We use a neural network consisting of two layers, a hidden layer of nonlinear neurodes and an output layer of one linear neurode. The network was trained using a back-propagation algorithm and a set of full-disk solar images obtained by HASTA (HSolar Telescope for Argentina), which is located at the Estación de Altura Ulrico Cesco of OAFA (Observatorio Astronómico Félix Aguilar), El Leoncito, San Juan, Argentina. This method is appropriate for the detection of solar flares in the complete optical classification, being portable to any Hinstrument and providing unique criteria for flare detection independent of the observer.     

Automatic Solar Filament Detection Using Image Processing Techniques

We present an automatic solar filament detection algorithm based on image enhancement, segmentation, pattern recognition, and mathematical morphology methods. This algorithm cannot only detect filaments, but can also identify spines, footpoints, and filament disappearances. It consists of five steps: (1) The stabilized inverse diffusion equation (SIDE) is used to enhance and sharpen filament contours. (2) A new method for automatic threshold selection is proposed to extract filaments from local background. (3) The support vector machine (SVM) is used to differentiate between sunspots and filaments. (4) Once a filament is identified, morphological thinning, pruning, and adaptive edge linking methods are used to determine the filament properties. (5) Finally, we propose a filament matching method to detect filament disappearances. We have successfully applied the algorithm to Hα full-disk images obtained at Big Bear Solar Observatory (BBSO). It has the potential to become the foundation of an automatic solar filament detection system, which will enhance our capabilities of forecasting and predicting geo-effective events and space weather.     

Automated Coronal Hole Detection Using Local Intensity Thresholding Techniques

We identify coronal holes using a histogram-based intensity thresholding technique and compare their properties to fast solar wind streams at three different points in the heliosphere. The thresholding technique was tested on EUV and X-ray images obtained using instruments onboard STEREO, SOHO and Hinode. The full-disk images were transformed into Lambert equal-area projection maps and partitioned into a series of overlapping sub-images from which local histograms were extracted. The histograms were used to determine the threshold for the low intensity regions, which were then classified as coronal holes or filaments using magnetograms from the SOHO/MDI. For all three instruments, the local thresholding algorithm was found to successfully determine coronal hole boundaries in a consistent manner. Coronal hole properties extracted using the segmentation algorithm were then compared with in situ measurements of the solar wind at ～?1 AU from ACE and STEREO. Our results indicate that flux tubes rooted in coronal holes expand super-radially within 1 AU and that larger (smaller) coronal holes result in longer (shorter) duration high-speed solar wind streams.     

Web-Based Data Processing System for Automated Detection of Oscillations with Applications to the Solar Atmosphere

A web-based, interactive system for the remote processing of imaging data sets (i.e., EUV, X-ray, and microwave) and the automated interactive detection of wave and oscillatory phenomena in the solar atmosphere is presented. The system targets localized, but spatially resolved, phenomena such as kink, sausage, and longitudinal propagating and standing waves. The system implements the methods of Periodmapping for pre-analysis, and Pixelized Wavelet Filtering for detailed analysis of the imaging data cubes. The system is implemented on the dedicated data-processing server , which is situated at the Institute of Solar-Terrestrial Physics, Irkutsk, Russia. Input data in the .sav, .fits, or .txt formats can be submitted via the local and/or global network (the Internet). The output data can be in the png, jpeg, and binary formats, on the user’s request. The output data are periodmaps; narrowband amplitude, power, phase and correlation maps of the wave’s sources at significant harmonics and in the chosen spectral intervals, and mpeg movies of their evolution. The system was tested by the analysis of the EUV and microwave emission from the active region NOAA 10756 on 4 May 2005 observed with TRACE and the Nobeyama Radioheliograph. The similarity of the spatial localization of three-minute propagating waves, near the footpoint of locally open magnetic-field lines determined by the potential-field extrapolation, in both the transition region and the corona was established. In the transition region the growth of the three-minute amplitude was found to be accompanied by the decrease in the line-of-sight angle to the wave-propagation direction.     

Modeling Total Solar Irradiance Variations Using Automated Classification Software on Mount Wilson Data

We present the results using the AutoClass analysis application available at NASA/Ames Intelligent Systems Div. (2002) which is a Bayesian, finite mixture model classification system developed by Cheeseman and Stutz (1996). We apply this system to Mount Wilson Solar Observatory (MWO) intensity and magnetogram images and classify individual pixels on the solar surface to calculate daily indices that are then correlated with total solar irradiance (TSI) to yield a set of regression coefficients. This approach allows us to model the TSI with a correlation of better than 0.96 for the period 1996 to 2007. These regression coefficients applied to classified pixels on the observed solar surface allow the construction of images of the Sun as it would be seen by TSI measuring instruments like the Solar Bolometric Imager recently flown by Foukal et al. (Astrophys. J. 611, L57, 2004). As a consequence of the very high correlation we achieve in reproducing the TSI record, our approach holds out the possibility of creating an on-going, accurate, independent estimate of TSI variations from ground-based observations which could be used to compare, and identify the sources of disagreement among, TSI observations from the various satellite instruments and to fill in gaps in the satellite record. Further, our spatially-resolved images should assist in characterizing the particular solar surface regions associated with TSI variations. Also, since the particular set of MWO data on which this analysis is based is available on a daily basis back to at least 1985, and on an intermittent basis before then, it will be possible to estimate the TSI emission due to identified solar surface features at several solar minima to constrain the role surface magnetic effects have on long-term trends in solar energy output.     

An Automated Algorithm to Distinguish and Characterize Solar Flares and Associated Sequential Chromospheric Brightenings

We present a new automated algorithm to identify, track, and characterize small-scale brightening associated with solar eruptive phenomena observed in Hα. The temporal, spatially localized changes in chromospheric intensities can be separated into two categories: flare ribbons and sequential chromospheric brightenings (SCBs). Within each category of brightening we determine the smallest resolvable locus of pixels, a kernel, and track the temporal evolution of the position and intensity of each kernel. This tracking is accomplished by isolating the eruptive features, identifying kernels, and linking detections between frames into trajectories of kernels. We fully characterize the evolving intensity and morphology of the flare ribbons by observing the tracked flare kernels in aggregate. With the location of SCB and flare kernels identified, they can easily be overlaid on complementary data sets to extract Doppler velocities and magnetic-field intensities underlying the kernels. This algorithm is adaptable to any dataset to identify and track solar features.     

Features of the Solar Active Cycles

1　ＩｎｔｒｏｄｕｃｔｉｏｎＴｈｅｓｏｌａｒａｃｔｉｖｅｃｙｃｌｅｉｓｕｓｕａｌｌｙｄｅｓｃｒｉｂｅｄｗｉｔｈｔｈｅｒｅｌａｔｉｖｅｓｕｎｓｐｏｔｎｕｍｂｅｒｓ.Ａｎａｌｙｓｅｓｏｆｈｉｓ ｔｏｒｉｃａｌｄａｔａｏｎｔｈｅｒｅｌａｔｉｖｅｓｕｎｓｐｏｔｎｕｍｂｅｒｓｈａｖｅｒｅｖｅａｌｅｄａｗｅａｌｔｈｏｆｉｎｆｏｒｍａｔｉｏｎａｂｏｕｔｔｈｅｓｏｌａｒａｃｔｉｖｅｃｙｃｌｅ (ＨｏｎｇＱｉｎｆａｎｇ 1 990 ,1 994;ＺｈｏｎｇＳｈｕｈｕａ 1 991 ,1 995 ) .Ｔｈｅｓｏ ｃａｌｌｅｄ 1 1 ｙｅａｒｐｅ ｒｉｏ…     

用日全食光谱探测太阳中低层大气对局部热动平衡的偏离

在太阳大气不同层次连续光谱中叠加有丰富的发射线或吸收线,对这些谱线轮廓进行反演分析可以探测太阳大气的化学成分和物理状态.太阳大气的色球及过渡区由于其密度低难以建立热动平衡,建立相应的大气模型需要采用非局部热动平衡(Non-Local Thermodynamic Equilibrium,N-LTE)理论.根据相对偏离因子计算来研究太阳中低层大气偏离局部热动平衡(Local Thermodynamic Equilibrium,LTE)分布的情况.首先对日全食观测过程中得到色球和过渡区不同高度形成的两条光谱数据进行反演,得到确定观测谱线的参数信息,如连续谱源函数、谱线源函数、多普勒宽度和由此推出的等效动力学温度;根据这些反演出的谱线参量计算出二维视场内每个空间采样点偏离LTE状态的定量结果;其次,根据用于观测的积分视场单元光纤排布阵列重构出辐射强度、等效动力学温度和相对偏离因子二维分布.结果显示:在局部小区域,温度分布和相对偏离因子的分布存在较强相关性,而与辐射强度分布无明显相关.从两条谱线导出的等效温度和相对偏离因子分布存在明显的差异.这两种二维分布揭示出太阳大气某些小尺度区域具有较强的结构性和复杂性,为进一步理解太阳中低层大气物理提供了一种新的视角.     

Automated Detection of Coronal Loops Using a Wavelet Transform Modulus Maxima Method

We propose and test a wavelet transform modulus maxima method for the automated detection and extraction of coronal loops in extreme ultraviolet images of the solar corona. This method decomposes an image into a number of size scales and tracks enhanced power along each ridge corresponding to a coronal loop at each scale. We compare the results across scales and suggest the optimum set of parameters to maximize completeness, while minimizing detection of noise. For a test coronal image, we compare the global statistics (e.g. number of loops at each length) to previous automated coronal-loop detection algorithms.     

Automated Solar Flare Detection and Feature Extraction in High-Resolution and Full-Disk H$\upalpha$ Images

In this article, an automated solar flare detection method applied to both full-disk and local high-resolution H\(\upalpha\) images is proposed. An adaptive gray threshold and an area threshold are used to segment the flare region. Features of each detected flare event are extracted, e.g. the start, peak, and end time, the importance class, and the brightness class. Experimental results have verified that the proposed method can obtain more stable and accurate segmentation results than previous works on full-disk images from Big Bear Solar Observatory (BBSO) and Kanzelhöhe Observatory for Solar and Environmental Research (KSO), and satisfying segmentation results on high-resolution images from the Goode Solar Telescope (GST). Moreover, the extracted flare features correlate well with the data given by KSO. The method may be able to implement a more complicated statistical analysis of H\(\upalpha\) solar flares.     

Advanced Automated Solar Filament Detection And Characterization Code: Description, Performance, And Results

We present a code for automated detection, classification, and tracking of solar filaments in full-disk Hα images that can contribute to Living With a Star science investigations and space weather forecasting. The program can reliably identify filaments; determine their chirality and other relevant parameters like filament area, length, and average orientation with respect to the equator. It is also capable of tracking the day-by-day evolution of filaments while they travel across the visible disk. The code was tested by analyzing daily Hα images taken at the Big Bear Solar Observatory from mid-2000 until beginning of 2005. It identified and established the chirality of thousands of filaments without human intervention. We compared the results with a list of filament proprieties manually compiled by Pevtsov, Balasubramaniam and Rogers (2003) over the same period of time. The computer list matches Pevtsov's list with a 72% accuracy. The code results confirm the hemispheric chirality rule stating that dextral filaments predominate in the north and sinistral ones predominate in the south. The main difference between the two lists is that the code finds significantly more filaments without an identifiable chirality. This may be due to a tendency of human operators to be biased, thereby assigning a chirality in less clear cases, while the code is totally unbiased. We also have found evidence that filaments obeying the chirality rule tend to be larger and last longer than the ones that do not follow the hemispherical rule. Filaments adhering to the hemispheric rule also tend to be more tilted toward the equator between latitudes 10^° and 30^°, than the ones that do not.     

Automatic Solar Flare Detection Using MLP,RBF, and SVM

The focus of automatic solar-flare detection is on the development of efficient feature-based classifiers. The three principal techniques used in this work are multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) classifiers. We have experimented and compared these three methods for solar-flare detection on solar H images obtained from the Big Bear Solar Observatory in California. The preprocessing step is to obtain nine principal features of the solar flares for the classifiers. Experimental results show that by using SVM we can obtain the best classification rate of the solar flares. We believe our work will lead to real-time solar-flare detection using advanced pattern recognition techniques.     

Detection of Solar Neutrinos

The present status of solar neutrino detection is reviewed. Results from the Homestake, Kamiokande, Super-Kamiokande, GALLEX and SAGE detectors all show a deficit when compared to recent standard solar model calculations. Two of these detectors, GALLEX and SAGE, have recently been checked with artificial 51Cr neutrino sources. It is shown that astrophysical scenarios to solve the solar neutrino problems are not favoured by the data. There is hope that the results of Super-Kamiokande and the forthcoming solar neutrino experiments can provide the answers to the open questions. This revised version was published online in July 2006 with corrections to the Cover Date.     

Automated Detection of EUV Prominences

We present methods to detect automatically off-limb prominences in the extreme ultraviolet (EUV), using synoptic images taken by the extreme-ultraviolet imaging telescope (EIT) on board SOHO. The 304 Å line is essential for the detection of EUV prominences, but the optimal detection is achieved through a combined image processing of the four synoptic EIT images. In addition, the difference between consecutive 304 Å images serves to identify erupted prominences. Representation maps of the quiescent EUV prominences for a given Carrington rotation are generated and used for further analysis of the detected structures. Longitudinal profiles of long-lived prominences are investigated for three examples at different latitudes, in conjunction with on-disk intensity profiles in the EUV. The observations coincide with theoretically predicted apparent longitudinal profiles, which can be distinguished from the profile of a prominence rising before eruption. The developed algorithms may be relevant to study the 3D geometry of features seen in the EUV and may facilitate the analysis of data from the future STEREO mission.     

基于图像的天体搜索研究与自动化集成软件开发

天体搜索是天文数据处理流程的一个重要环节,也是以平方公里阵列射电望远镜(Square Kilometre Array, SKA)为代表的下一代射电望远镜在面向海量数据处理中的挑战之一.现今天体自动搜索算法、软件已日趋成熟并投入应用,不过在自动化、兼容性等方面仍具有提升空间.以更自动化、更适应海量数据需求的天体搜索算法研究为宗旨,以现有算法为研究基础,天体自动搜索软件系统得到设计和开发.该系统包含友好的交互式用户操作界面,具备可视化输出数据显示、兼容不同数据输入和输出并包含为实际应用服务的文件管理功能.该系统对于大天区图以及图像集,均能够很好地进行自动化处理.测试结果显示,上述方法对于天体搜索的改进有一定成效.后续将在此基础上对该集成系统做进一步的改进开发,以适应更多的需求.     

Peculiar Features in the Solar Coronal Hole Occurrence

A good knowledge of coronal hole (CH) evolution in time is relevant for the understanding of the decay and/or the stability of large-scale magnetic fields in the different heliographic latitudinal belts. Using a CH catalogue, mainly compiled from the Hei line (10830 Å) measurements (Sanchez-Ibarra and Barraza-Paredes, 1992, and updated from the NOAA/Boulder Web pages), some characteristics of the CH behaviour in the solar activity cycles 21 and 22 are identified and described. We found: (i) the total number distributions for the isolated and polar coronal holes are similar, (ii) there is a north/south asymmetry, with a northern dominance in the number distribution of the polar coronal holes, (iii) a hint of a 22-year periodicity in the CH behaviour. In addition, two pairs of two isolated CHs with opposite polarity, maximum ages of more than 14 Carrington rotations, separated by about two years, are found during the early decreasing activity phase of each cycle.     

Prediction of Solar Cycle Maximum Using Solar Cycle Lengths

If the rise time RT, fall time FT, and total time TT (i.e., RT+FT) of a solar cycle are compared against the maximum amplitude Rz(max ) for the following cycle, then only the association between TT and Rz(max ) is inferred to be well anticorrelated, inferring that the larger (smaller) the value of Rz(max ) for the following cycle, the shorter (longer) the TT of the preceding cycle. Although the inferred correlation (−0.68) is statistically significant, the inferred standard error of estimate is quite large, so predictions using the inferred correlation are not very precise. Removal of cycle pairs 15/16, 19/20, and 20/21 (statistical outliers) yields a regression that is highly statistically significant (−0.85) and reduces the standard error of estimate by 18%. On the basis of the adjusted regression and presuming TT=140 months for cycle 23, the present ongoing cycle, cycle 24’s 90% prediction interval for Rz(max ) is estimated to be about 94±44, inferring only a 5% probability that its Rz(max ) will be larger than about 140, unless of course cycle pair 23/24 is a statistical outlier.