Developing an Advanced Automated Method for Solar Filament Recognition and Its Scientific Application to a Solar Cycle of MLSO Hα Data

We developed a method to automatically detect and trace solar filaments in Hα full-disk images. The program is able not only to recognize filaments and determine their properties, such as the position, the area, the spine, and other relevant parameters, but also to trace the daily evolution of the filaments. The program consists of three steps: First, preprocessing is applied to correct the original images; second, the Canny edge-detection method is used to detect filaments; third, filament properties are recognized through morphological operators. To test the algorithm, we successfully applied it to observations from the Mauna Loa Solar Observatory (MLSO). We analyzed Hα images obtained by the MLSO from 1998 to 2009 and obtained a butterfly diagram of filaments. This shows that the latitudinal migration of solar filaments has three trends in Solar Cycle 23: The drift velocity was fast from 1998 to the solar maximum, after which it became relatively slow. After 2006, the migration became divergent, signifying the solar minimum. About 60 % of the filaments with latitudes higher than 50^° migrate toward the polar regions with relatively high velocities, and the latitudinal migrating speeds in the northern and the southern hemispheres do not differ significantly in Solar Cycle 23.     

Automated Detection of Filaments and Their Disappearance Using Full-Disc Hα Images

A new algorithm is presented that automatically detects filaments on the Sun in full-disc Hα images. Pre-processing of Hα images includes corrections for limb darkening and foreshortening. Further, by applying suitable intensity and size thresholds, filaments are extracted, while other solar features, e.g. sunspots and plages, are removed. Filament attributes such as their position on the solar disc, total area, length, and number of fragments are determined. In addition, every filament is also labelled with a unique number for identification. The algorithm is capable of following a particular filament through successive images, which allows us to detect their changes and disappearance. We have analysed ten cases of filament eruption from different observatories, and the results obtained are presented. The algorithm will eventually be integrated with an upcoming telescope at the Udaipur Solar Observatory for real-time monitoring of activated/eruptive filaments. This aspect should prove to be of particular importance in studies pertaining to space weather.     

Imaging Spectroscopy of a Solar Filament Using a Tunable Hα Filter

Observations using a narrow band Hα filter still remain one of the best ways to investigate the fine structures and internal dynamics of solar filaments. Hα observations, however, have been usually carried out with the peak response of the filter fixed at a single wavelength, usually at the centerline, in which the investigation is limited to the Hα morphology and its time evolution. In this paper, we demonstrate that the Hα spectroscopy that takes Hα images successively at several wavelengths is a useful tool in the study of solar filaments on the solar disk. Our observation of a filament was carried out on August 3, 2004 at Big Bear Solar Observatory using the 10-inch refractor. The Lyot Hα filter was successively tuned to five wavelengths: ?0.6, ?0.3, 0.0, +0.3, and +0.6 Å from the Hα line center. Each set of wavelength scan took 15 s. After several steps of data reduction, we have constructed a five-wavelength spectral profile of intensity contrast at every spatial point. The contrast profile at each spatial point inside the filament was reasonably well fit by the cloud model as far as the contrast is high enough, and allowed us to construct the maps of τ₀, v, Δ λ_D and S in the filament. We also found that the line center method that is often used, always yields line-of-sight velocities that are systematically lower than the cloud model fit. Our result suggests that taking Hα images at several wavelengths using a tunable filter provides an effective way of deriving physically meaningful parameters of solar filaments. Particularly constructing the time sequence of v maps appears to be a useful tool for the study of internal dynamics, like counterstreaming, in filaments.     

Computer Vision for the Solar Dynamics Observatory (SDO)

In Fall 2008 NASA selected a large international consortium to produce a comprehensive automated feature-recognition system for the Solar Dynamics Observatory (SDO). The SDO data that we consider are all of the Atmospheric Imaging Assembly (AIA) images plus surface magnetic-field images from the Helioseismic and Magnetic Imager (HMI). We produce robust, very efficient, professionally coded software modules that can keep up with the SDO data stream and detect, trace, and analyze numerous phenomena, including flares, sigmoids, filaments, coronal dimmings, polarity inversion lines, sunspots, X-ray bright points, active regions, coronal holes, EIT waves, coronal mass ejections (CMEs), coronal oscillations, and jets. We also track the emergence and evolution of magnetic elements down to the smallest detectable features and will provide at least four full-disk, nonlinear, force-free magnetic field extrapolations per day. The detection of CMEs and filaments is accomplished with Solar and Heliospheric Observatory (SOHO)/Large Angle and Spectrometric Coronagraph (LASCO) and ground-based Hα data, respectively. A?completely new software element is a trainable feature-detection module based on a generalized image-classification algorithm. Such a trainable module can be used to find features that have not yet been discovered (as, for example, sigmoids were in the pre-Yohkoh era). Our codes will produce entries in the Heliophysics Events Knowledgebase (HEK) as well as produce complete catalogs for results that are too numerous for inclusion in the HEK, such as the X-ray bright-point metadata. This will permit users to locate data on individual events as well as carry out statistical studies on large numbers of events, using the interface provided by the Virtual Solar Observatory. The operations concept for our computer vision system is that the data will be analyzed in near real time as soon as they arrive at the SDO Joint Science Operations Center and have undergone basic processing. This will allow the system to produce timely space-weather alerts and to guide the selection and production of quicklook images and movies, in addition to its prime mission of enabling solar science. We briefly describe the complex and unique data-processing pipeline, consisting of the hardware and control software required to handle the SDO data stream and accommodate the computer-vision modules, which has been set up at the Lockheed-Martin Space Astrophysics Laboratory (LMSAL), with an identical copy at the Smithsonian Astrophysical Observatory (SAO).     

Automatic Extraction of Filaments in Hα Solar Images

This paper presents a new method which allows for the automatic extraction and tracking of the evolution of filaments in solar images. Series of Hα full-disk images are taken in regular time intervals to observe the changes of the solar disk features. In each picture, the solar chromosphere filaments are identified for further evolution examination. Two alternative preprocessing techniques converting grayscale images into black-and-white pictures with enhanced chromosphere granularity are examined: local thresholding based on median values and global thresholding with brightness and area normalization. The next step employs morphological closing operations with multi-directional linear structuring elements to extract elongated shapes in the image. After logical intersection of directional filtering results, remaining noise is removed from the final outcome using morphological dilation and erosion with a circular structuring element. Experimental results show that the developed technique can achieve excellent results in detecting large filaments and good detection rates for small filaments.     

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.     

基于改进VNet太阳暗条检测方法

太阳暗条作为太阳大气磁场的示踪,对研究太阳磁场有极其重要的意义。针对现有的暗条检测方法存在检测精度不高,弱小暗条错检、漏检等问题,提出一种基于改进VNet网络的太阳暗条检测方法。首先,使用大熊湖天文台Hα全日面图像并结合磁图制作了太阳暗条数据集;其次,在VNet网络下采样部分采用Inception模块融合不同尺度特征图的特征,同时加入注意力机制增强特征图中暗条部分的语义信息;最后在上采样部分引入深度监督模块,更多地保留太阳暗条的细节特征。为验证算法性能,采用191幅Hα全日面图像数据集,其中包含暗条共3372条。算法在测试数据集上平均准确率达到0.9883,F1值达到0.8385。实验结果证明,该方法可以有效识别Hα全日面图中的暗条。     

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.     

On the possible mechanism of formation of emission rim in hydrogen filaments

Hα filtergrams of the chromosphere show an emission rim in many hydrogen filaments. We suppose that formation of this rim is due to photospheric radiation reflected by the filament in the direction of the chromosphere. The calculations show that: (1) the maximum contrast of the rim relative to the undisturbed chromosphere amounts to 1.4; (2) the larger the optical thickness of the filament and the closer to the solar limb it is situated, the brighter and wider is the rim; (3) the rim was not observed in filaments whose heights exceeds 10000km above the chromosphere. These results are in close agreement with observations.     

Active Region Detection and Verification With the Solar Feature Catalogue

We describe the automated extraction of active regions (ARs) or plages from the European Grid of Solar Observations (EGSO) Solar Feature Catalogue using a region-growing technique. In this work, Hα and Ca ii K3 solar images from the Meudon Observatory and EUV solar images from the SOHO/EIT instrument were used. For better detection accuracy, the statistical properties of each quarter of a full disk solar image are used to define local intensity thresholds for an initial segmentation that helps to define AR seeds. Median filtering and morphological operations are applied to the resulting binary image in order to remove noise and to merge broken regions. The centroids of each labelled region are used as seeds, from which a region-growing procedure starts. Statistics-based local thresholding is also applied to compute upper- and lower- threshold intensity values defining the spatial extents of the regions. The detection results obtained with the resulting automated thresholding and region-growing (ATRG) procedure are compared day-by-day with the synoptic maps manually generated by the Meudon Observatory and NOAA for 2 months in 2002 and more coarsely over a 5-year period. The moderate correlation found between our detection results and those produced manually on the other data sets reveals a need for a unified active region definition. As an application of the SFC for ARs we present the tracking of the active region AR NOAA 10484 during its appearance on the solar disk from 19–26 October 2003 and compare its intensity variations for Hα and Fe xii 195 Å wavelengths.     

Measurements of Filament Height in Hα and EUV 304 Å

In this study, we present the three-dimensional (3D) configuration of a filament observed by STEREO and the Global High Resolution H-alpha Network (GHN) in EUV 304 Å and Hα line center, respectively. This was the largest filament located close to the active region NOAA 10956 that produced a small B9.6 flare and two Coronal Mass Ejections (CMEs) on 19 May 2007. The 3D coordinates of multiple points traced along this filament were reconstructed by triangulation from two different aspect angles. The two STEREO (A and B) spacecraft had a separation angle α of 8.6 degree on 19 May 2007. The “true” heights of the filament were estimated using STEREO images in EUV 304 and Hα images, respectively. Our results show that EUV emission of the filament originates from higher locations than the Hα emission. We also compare the measured reconstructed heights of the filaments in EUV with those reported in previous studies.     

Multi-wavelength study of a high-latitude EUV filament

A large filament was observed during a multi-wavelength coordinated campaign on June 19, 1998 in the Hα line with the Swedish Vacuum Solar Telescope (SVST) at La Palma, in the coronal lines Fe ix/x 171 Å and Fe xi 195 Å with the Transition Region and Coronal Explorer (TRACE) and in EUV lines with the SOHO/CDS spectrometer and the hydrogen Lyman series with the SOHO/SUMER spectrometer. Because of its high-latitude location, it is possible to disentangle the physical properties of the Hα filament and the filament channel seen in EUV lines. TRACE images point out a dark region fitting the Hα fine-structure threads and a dark corridor (filament channel), well extended south of the magnetic inversion line. A similar pattern is observed in the CDS EUV-line images. The opacity of the hydrogen and helium resonance continua at 171 Å is almost two orders of magnitude lower than that at the Hi head (912 Å) and thus similar to the opacity of the Hα line. Since we do not see the filament channel in Hα, this would imply that it should also be invisible in TRACE lines. Thus, the diffuse dark corridor is interpreted as due to the coronal ‘volume blocking’ by a cool plasma which extends to large altitudes. Such extensions were also confirmed by computing the heights from the projection geometry and by simulations of the CDS and TRACE line intensities using the spectroscopic model of EUV filaments (Heinzel, Anzer, and Schmieder, 2003). Finally, our NLTE analysis of selected hydrogen Lyman lines observed by SUMER also leads to a conclusion that the dark filament channel is due to a presence of relatively cool plasma having low densities and being distributed at altitudes reaching the Hα filament.     

Investigation of the Differential Rotation by H�� Filaments and Long-Lived Magnetic Features for Solar Activity Cycles 20 and 21

For solar activity Cycles 20 and 21 (1966??C?1985) the solar differential rotation has been investigated using H?? filaments and relatively small-scale long-lived magnetic features with negative and positive polarities. We used annual averaged angular velocities of quiescent H?? filaments from H?? photoheliograms of the Abastumani Astrophysical Observatory film collection and selected long-lived magnetic features from the McIntosh atlas (McIntosh, Willock, and Thompson, Atlas of Stackplots, NGDC, 1991). We have determined coefficients of Faye??s formulas for H?? filaments as well as for long-lived magnetic features and have found that for Solar Cycles 20 and 21 the H?? filaments have lower rotation rates and rotated more differentially than the long-lived magnetic features.     

A giant bipolar shell around the WO star in the galaxy IC 1613: Structure and kinematics

Observations of the nebula associated with the WO star in the galaxy IC 1613 are presented. The observations were carried out with a scanning Fabry-Perot interferometer in Hα using the 6-m Special Astrophysical Observatory telescope; narrow-band Hα and [O III]images were obtained with the 4-m KPNO telescope (USA). The monochromatic Hα image clearly reveals a giant bipolar shell structure outside the bright nebula S3. The sizes of the southeastern and northwestern shells are 112×77 and (186–192)×(214–224) pc, respectively. We have studied the object’s kinematics for the first time and found evidence for expansion of both shells. The expansion velocities of the southeastern and northwestern shells exceed 50 and 70 km s^?1, respectively. We revealed a filamentary structure of the shells and several compact features in the S3 core. A scenario is proposed for the formation of the giant bipolar structure by the stellar wind from the central WO star located at the boundary of a “supercavity” in the galaxy’s H I distribution.     

Automatic Detection and Classification of Coronal Holes and Filaments Based on EUV and Magnetogram Observations of the Solar Disk

A new method for the automated detection of coronal holes and filaments on the solar disk is presented. The starting point is coronal images taken by the Extreme Ultraviolet Telescope on the Solar and Heliospheric Observatory (SOHO/EIT) in the Fe ix/x 171 Å, Fe xii 195 Å, and He ii 304 Å extreme ultraviolet (EUV) lines and the corresponding full-disk magnetograms from the Michelson Doppler Imager (SOHO/MDI) from different phases of the solar cycle. The images are processed to enhance their contrast and to enable the automatic detection of the two candidate features, which are visually indistinguishable in these images. Comparisons are made with existing databases, such as the He i 10830 Å NSO/Kitt Peak coronal-hole maps and the Solar Feature Catalog (SFC) from the European Grid of Solar Observations (EGSO), to discriminate between the two features. By mapping the features onto the corresponding magnetograms, distinct magnetic signatures are then derived. Coronal holes are found to have a skewed distribution of magnetic-field intensities, with values often reaching 100?–?200 gauss, and a relative magnetic-flux imbalance. Filaments, in contrast, have a symmetric distribution of field intensity values around zero, have smaller magnetic-field intensity than coronal holes, and lie along a magnetic-field reversal line. The identification of candidate features from the processed images and the determination of their distinct magnetic signatures are then combined to achieve the automated detection of coronal holes and filaments from EUV images of the solar disk. Application of this technique to all three wavelengths does not yield identical results. Furthermore, the best agreement among all three wavelengths and NSO/Kitt Peak coronal-hole maps occurs during the declining phase of solar activity. The He ii data mostly fail to yield the location of filaments at solar minimum and provide only a subset at the declining phase or peak of the solar cycle. However, the Fe ix/x 171 Å and Fe xii 195 Å data yield a larger number of filaments than the Hα data of the SFC.     

Comparisons of interplanetary type III storm footpoints with solar features

The trajectories of 38 type III storms in the interplanetary medium have been deduced from ISEE-3 radio observations and extrapolated back to the Sun to determine the Carrington coordinates of their footpoints. The analysis assumes radial motion of the solar wind, and the trajectories are projected radially back toward the surface for the last few solar radii. To identify the storm sources, the footpoints were compared to a variety of solar features: to the large-scale neutral line at the base of the current sheet, to active regions, to the small-scale neutral lines and Hα filaments which trace out active regions, and to coronal holes. Most of the footpoints were found to lie near active regions, in agreement with metric storm locations. There is a weak correlation with Hα filaments, no apparent association with the current sheet, and an anticorrelation with coronal holes. There is a small excess of storms in the leading half of magnetic sectors.     

SoFAST: Automated Flare Detection with the PROBA2/SWAP EUV Imager

The Sun Watcher with Active Pixels and Image Processing (SWAP) EUV imager onboard PROBA2 provides a non-stop stream of coronal extreme-ultraviolet (EUV) images at a cadence of typically 130 seconds. These images show the solar drivers of space-weather, such as flares and erupting filaments. We have developed a software tool that automatically processes the images and localises and identifies flares. On one hand, the output of this software tool is intended as a service to the Space Weather Segment of ESA’s Space Situational Awareness (SSA) program. On the other hand, we consider the PROBA2/SWAP images as a model for the data from the Extreme Ultraviolet Imager (EUI) instrument prepared for the future Solar Orbiter mission, where onboard intelligence is required for prioritising data within the challenging telemetry quota. In this article we present the concept of the software, the first statistics on its effectiveness and the online display in real time of its results. Our results indicate that it is not only possible to detect EUV flares automatically in an acquired dataset, but that quantifying a range of EUV dynamics is also possible. The method is based on thresholding of macropixelled image sequences. The robustness and simplicity of the algorithm is a clear advantage for future onboard use.     

Automation of the Filament Tracking in the Framework of the HELIO Project

We present a new method to automatically track filaments over the solar disk. The filaments are first detected on Meudon Spectroheliograph Hα images of the Sun, applying the technique developed by Fuller, Aboudarham, and Bentley (Solar Phys. 227, 61, 2005). This technique combines cleaning processes, image segmentation based on region growing, and morphological parameter extraction, including the determination of filament skeletons. The coordinates of the skeleton pixels, given in a heliocentric system, are then converted to a more appropriate reference frame that follows the rotation of the Sun surface. In such a frame, a co-rotating filament is always located around the same position, and its skeletons (extracted from each image) are thus spatially close, forming a group of adjacent features. In a third step, the shape of each skeleton is compared with its neighbours using a curve-matching algorithm. This step will permit us to define the probability [P] that two close filaments in the co-rotating frame are actually the same one observed on two different images. At the end, the pairs of features, for which the corresponding probability is greater than a threshold value, are associated using tracking identification indices. On a representative sample of filaments, the good agreement between automated and manual tracking confirms the reliability of the technique to be applied on large data sets. This code is already used in the framework of the Heliophysics Integrated Observatory (HELIO) to populate a catalogue dedicated to solar and heliospheric features (HFC). An extension of this method to other filament observations, and possibly sunspots, faculae, and coronal-holes tracking, can also be envisaged.     

A Study On Surges: I. Automatic Detection Of Dynamic Hα Dark Features From High-Cadence Full-Disk Observations

We present a new method for the automatic identification and classification of dynamic Hα dark features found in time series of full-disk solar images at three Hα wavelengths (center, and ± 0.8 Å). The simultaneous Hα observations are obtained by the multi-channel Flare Monitoring Telescope (FMT) at Hida Observatory. The program was developed in order to replace the present visual detection and classification of the phenomena. Usually, an obvious dark feature found in the Hα ?0.8 Å observations probably corresponds to some phenomenon such as a surge or chromospheric network enhancement, or filament activity. Thus, one of our aims in this program is to distinguish each phenomenon by its own properties and key parameters. We optimized the threshold values of the key parameters such as the area and darkness of the transiently darkening features in Hα ?0.8 Å so that the computer can reasonably identify surges and filament activations. In comparison, for a 7-day observation period, the number of dark events detected by the program contains 89% of the events recognized visually. However, 10 times more events are detected automatically. The missing events are mainly caused by the deletion of data with poor visibility. It is found that the dark events can be identified with more precise starting and ending times by a machine than by a human. Some statistical studies of surges or other activities can be carried out based on the computer-produced database. With some modifications the program can be applied to monitor real-time dynamic features on disk, including flare ribbons.     

Statistical Moments of Active-Region Images During Solar Flares

We present new temporal-evolution diagnostics of solar flares. The high-order statistical moments (skewness and kurtosis) of the Hα images of active regions during solar flares were computed from their initial phases up to their maxima. The same method was used for quiet active regions for tests and comparison. We found that temporal profiles of the Hα statistical moments during flares roughly correspond to those observed in soft X-rays by the GOES satellite. Maxima of the cross-correlation coefficients between the skewness and the GOES X-rays were found to be 0.82?–?0.98, and the GOES X-rays are delayed 0?–?144 seconds against the skewness. We recognized that these moments are very sensitive to pre-flare activities. Therefore we used them to determine the flare starting-time and to study the pre-flare quasi-periodic processes. We determined the periods of these pre-flare processes in an interval of 20?–?400 seconds by using special convolution filters and Fourier analysis. We propose to use this method to analyze active regions during the very early phases of solar flares, and even in real time.