Detection Of Coronal Mass Ejections Research Articles

A New Automatic Tool for CME Detection and Tracking with Machine-learning Techniques

Abstract With the accumulation of coronal mass ejection (CME) observations by coronagraphs, automatic detection and tracking of CMEs has proven to be crucial. The excellent performance of the convolutional neural network in image classification, object detection, and other computer vision tasks motivates us to apply it to CME detection and tracking as well. We developed a new tool for CME Automatic detection and tracking with MachinE Learning (CAMEL) techniques. The system is a three-module pipeline. It is first a supervised image classification problem. We solve it by training a neural network LeNet with training labels obtained from an existing CME catalog. Those images containing CME structures are flagged as CME images. Next, to identify the CME region in each CME-flagged image, we use deep descriptor transforming to localize the common object in an image set. A following step is to apply the graph cut technique to finely tune the detected CME region. To track the CME in an image sequence, the binary images with detected CME pixels are converted from a cartesian to a polar coordinate. A CME event is labeled if it can move in at least two frames and reach the edge of the coronagraph field of view. For each event, a few fundamental parameters are derived. The results of four representative CMEs with various characteristics are presented and compared with those from four existing automatic and manual catalogs. We find that CAMEL can detect more complete and weaker structures and has better performance to catch a CME as early as possible.

On the Coronal Mass Ejection Detection Rate during Solar Cycles 23 and 24

Abstract The Solar and Heliospheric Observatory (SOHO) mission’s white light coronagraphs have observed more than 25,000 coronal mass ejections (CMEs) from 1996 January to the end of 2015 July. This period of time covers almost two solar cycles (23 and 24). The basic attributes of CMEs, reported in the SOHO/Large Angle and Spectrometric Coronagraph (LASCO) catalog, during these solar cycles were statistically analyzed. The question of the CME detection rate and its connection to the solar cycles was considered in detail. Based on the properties and detection rate, CMEs can be divided into two categories: regular and specific events. The regular events are pronounced and follow the pattern of sunspot number. On the other hand, the special events are poorer and more correlated with the general conditions of heliosphere and corona. Nevertheless, both groups of CMEs are the result of the same physical phenomenon, viz. release of magnetic energy from closed field regions. It was demonstrated that the enhanced CME rate, since the solar cycle 23 polar-field reversal, is due to a significant decrease of total (magnetic and plasma) heliospheric pressure as well as the changed magnetic pattern of solar corona. CMEs expel free magnetic energy and helicity from the Sun; therefore, they are related to complex solar magnetic field structure. It is also worth emphasizing that the CMEs listed in the SOHO/LASCO catalog are real ejections (not false identification). Their detection rate reflects the global evolution of the magnetic field on the Sun, and not only changes in the magnetic structures associated with sunspots.

Single and Multiwavelength Detection of Coronal Dimming and Coronal Wave Using Faster R-CNN

Automatic detection of solar events, especially uncommon events such as coronal dimming (CD) and coronal wave (CW), is very important in solar physics research. The CD and CW are not only related to the detection of coronal mass ejections (CMEs) but also affect space weather. In this paper, we have studied methods for automatically detecting them. In addition, we have collected and processed a dataset that includes the solar images and event records, where the solar images come from the Atmospheric Imaging Assembly (AIA) of Solar Dynamics Observatory (SDO) and the event records come from Heliophysics Event Knowledgebase (HEK). Different from the methods used before, we introduce the idea of deep learning. We train single-wavelength and multiwavelength models based on Faster R-CNN. In terms of accuracy, the single-wavelength model performs better. The multiwavelength model has a better detection performance on multiple solar events than the single-wavelength model.

The quest for stellar coronal mass ejections in late-type stars

Context. Flares and coronal mass ejections (CMEs) can have deleterious effects on their surroundings: they can erode or completely destroy atmospheres of orbiting planets over time and also have high importance in stellar evolution. Most of the CME detections in the literature are single events found serendipitously sparse for statistical investigation. Aims. We aimed to gather a large amount of spectral data of M-dwarfs to drastically increase the number of known events to make statistical analysis possible in order to study the properties of potential stellar CMEs. Methods. Using archival spectral data we investigated asymmetric features of Balmer-lines, which could indicate the Doppler-signature of ejected material. Results. Of more than 5500 spectra we find 478 that have line asymmetries – including nine larger events, in terms of velocity and mass – on 25 objects, with 1.2–19.6 events per day on objects with line asymmetries. Most events are connected with enhanced peaks of Balmer-lines, indicating that these are connected to flares similar to solar events. In most cases the detected speed does not reach surface escape velocity: the typical observed maximum velocities are on the order of 100–300 km s−1, while the typical masses of the ejecta were on the order of 1015−1018 g. Statistical analysis of the events suggests that these events are more frequent on cooler stars with stronger chromospheric activity. Conclusions. If the detected events correspond to CMEs, the detected maximum velocities are lower than those observed on the Sun, while event rates were somewhat lower than we could expect from the solar case. If the velocities are not distorted significantly due to a projection effect, these findings may support the idea that most of the coronal mass ejections could be suppressed by a strong magnetic field. Alternatively, it is possible that we can observe only an early low-coronal phase of the events before being accelerated at higher altitudes. Our findings could indicate that later-type, active dwarfs could be a safer environment for exoplanetary systems CME-wise than previously thought, and atmosphere loss due to radiation effects would play a stronger role in exoplanetary atmosphere evolution than CMEs.

Regularities in the Formation of Coronal Mass Ejections Associated and not Associated with Type II Radio Bursts

Regularities in the formation of coronal mass ejections (CMEs) associated (Radio Loud, RL CME) and not associated (Radio Quiet, RQ CME) with type II radio bursts (RBIIs) in cycles 23 and 24 are reviewed. It is shown that the number of observed RQ and RL CME events and their parameters are quite different in cycles 23 and 24. The influence of the magnetic field and plasma parameters on the number of observed RQ and RL CME events and their parameters have been studied. The magnetic field in the region of RL CME detection was computed based on the observed, large-scale, photospheric magnetic field of the Sun, while we used direct observations of the plasma density in the Earth’s orbit to calculate for the plasma density. This enabled us to obtain the Alfven speed and to examine the influence of cyclical variation of the magnetic field, plasma density, and Alfven speed on the number of events and RQ and RL CME parameters. The results show that magnetic field and plasma values may be among the key factors determining the conditions, which are favorable for the formation of RQ CMEs or RL CMEs.

Observed versus modelled stellar CME rates

AbstractStellar coronal mass ejections (CMEs) may play an important role in stellar and planetary evolution, therefore the knowledge on parameter distributions of this energetic activity phenomenon is highly relevant. During the last years several attempts have been made to detect stellar CMEs of late-type main-sequence and pre main-sequence stars from dedicated optical spectroscopic observations. Up to now only a handful of distinct stellar CME detections are known which contradicts the results from stellar CME modelling, which predict higher CME rates. We report on dedicated ongoing and future observational attempts to detect stellar CMEs and discuss the observational results with respect to the results from stellar CME modelling.

Onboard Automated CME Detection Algorithm for the Visible Emission Line Coronagraph on ADITYA-L1

ADITYA-L1 is India's first space mission to study the Sun from Lagrangian 1 position. { \textit{Visible Emission Line Coronagraph}} (VELC) is one of the seven payloads in ADITYA-L1 mission 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 arcsec pixel$^{-1}$) images of corona from 1.05 R$_{\odot}$ -- 3 R$_{\odot}$ at high cadence of 1 s in 10 \AA\ passband centered at 5000 \AA. Due to limited telemetry at Lagrangian 1 position we plan to implement an onboard automated CME detection algorithm. The detection algorithm is based on the intensity thresholding followed by the area thresholding in successive difference images spatially re-binned to improve signal to noise ratio. We present the results of the application of this algorithm on the data from existing space- and ground-based coronagraph. Since, no existing space-based coronagraph has FOV similar to VELC, we have created synthetic coronal images for VELC FOV after including photon noise and injected different type of CMEs. The performance of CME detection algorithm is tested on these images. We found that for VELC images, the telemetry can be reduced by a factor of 85\% or more keeping CME detection rate of 70\% or above at the same time. Finally, we discuss the advantages and disadvantages of this algorithm. The application of such onboard algorithm in future will enable us to take higher resolution images with improved cadence from space and also reduce the load on limited telemetry at the same time. This will help in better understanding of CMEs by studying their characteristics with improved spatial and temporal resolutions.

CMEs in the Heliosphere: I. A Statistical Analysis of the Observational Properties of CMEs Detected in the Heliosphere from 2007 to 2017 by STEREO/HI-1

We present a statistical analysis of coronal mass ejections (CMEs) imaged by the Heliospheric Imager (HI) instruments on board NASA’s twin-spacecraft STEREO mission between April 2007 and August 2017 for STEREO-A and between April 2007 and September 2014 for STEREO-B. The analysis exploits a catalogue that was generated within the FP7 HELCATS project. Here, we focus on the observational characteristics of CMEs imaged in the heliosphere by the inner (HI-1) cameras, while following papers will present analyses of CME propagation through the entire HI fields of view. More specifically, in this paper we present distributions of the basic observational parameters – namely occurrence frequency, central position angle (PA) and PA span – derived from nearly 2000 detections of CMEs in the heliosphere by HI-1 on STEREO-A or STEREO-B from the minimum between Solar Cycles 23 and 24 to the maximum of Cycle 24; STEREO-A analysis includes a further 158 CME detections from the descending phase of Cycle 24, by which time communication with STEREO-B had been lost. We compare heliospheric CME characteristics with properties of CMEs observed at coronal altitudes, and with sunspot number. As expected, heliospheric CME rates correlate with sunspot number, and are not inconsistent with coronal rates once instrumental factors/differences in cataloguing philosophy are considered. As well as being more abundant, heliospheric CMEs, like their coronal counterparts, tend to be wider during solar maximum. Our results confirm previous coronagraph analyses suggesting that CME launch sites do not simply migrate to higher latitudes with increasing solar activity. At solar minimum, CMEs tend to be launched from equatorial latitudes, while at maximum, CMEs appear to be launched over a much wider latitude range; this has implications for understanding the CME/solar source association. Our analysis provides some supporting evidence for the systematic dragging of CMEs to lower latitude as they propagate outwards.

Connecting Coronal Mass Ejections to Their Solar Active Region Sources: Combining Results from the HELCATS and FLARECAST Projects

Coronal mass ejections (CMEs) and other solar eruptive phenomena can be physically linked by combining data from a multitude of ground-based and space-based instruments alongside models, however this can be challenging for automated operational systems. The EU Framework Package 7 HELCATS project provides catalogues of CME observations and properties from the Helio- spheric Imagers onboard the two NASA/STEREO spacecraft in order to track the evolution of CMEs in the inner heliosphere. From the main HICAT catalogue of over 2,000 CME detections, an automated algorithm has been developed to connect the CMEs observed by STEREO to any corresponding solar flares and active region (AR) sources on the solar surface. CME kinematic properties, such as speed and angular width, are compared with AR magnetic field properties, such as magnetic flux, area, and neutral line characteristics. The resulting LOWCAT catalogue is also compared to the extensive AR property database created by the EU Horizon 2020 FLARECAST project, which provides more complex magnetic field parameters derived from vector magnetograms. Initial statistical analysis has been undertaken on the new data to provide insight into the link between flare and CME events, and characteristics of eruptive ARs. Warning thresholds determined from analysis of the evolution of these parameters is shown to be a useful output for operational space weather purposes. Parameters of particular interest for further analysis include total unsigned flux, vertical current, and current helicity. The automated method developed to create the LOWCAT catalogue may also be useful for future efforts to develop operational CME forecasting.

Automated detection of Coronal Mass Ejections in Visible Emission Line Coronagraph (VELC) on-board ADITYA-L1

AbstractAn onboard automated coronal mass ejections (CMEs) detection algorithm has been developed for Visible Emission Line Coronagraph (VELC) onboard ADITYA-L1. The aim of this algorithm is to reduce the load on telemetry by sending the high spatial (~ 2.51 arcsec pixel−1) and temporal (1 s) resolution images of corona from 1.05 R⊙ to 3 R⊙, containing CMEs and rejecting others. It is based on intensity thresholding followed by an area thresholding in successive running difference images which are re-binned to lower resolution to improve signal to noise. Here we present the results of application of the algorithm on synthetic corona images generated for the VELC field of view (FOV).

Automatic Near‐Real‐Time Detection of CMEs in Mauna Loa K‐Cor Coronagraph Images

AbstractA simple algorithm has been developed to detect the onset of coronal mass ejections (CMEs), together with speed estimates, in near‐real time using linearly polarized white‐light solar coronal images from the Mauna Loa Solar Observatory K‐Cor telescope. Ground observations in the low corona can warn of CMEs well before they appear in space coronagraphs. The algorithm used is a variation on the Solar Eruptive Event Detection System developed at George Mason University. It was tested against K‐Cor data taken between 29 April 2014 and 20 February 2017, on days identified as containing CMEs. This resulted in testing of 139 days' worth of data containing 171 CMEs. The detection rate varied from close to 80% when solar activity was high down to as low as 20–30% when activity was low. The difference in effectiveness with solar cycle is attributed to the relative prevalence of strong CMEs between active and quiet periods. There were also 12 false detections, leading to an average false detection rate of 8.6%. The K‐Cor data were also compared with major solar energetic particle (SEP) storms during this time period. There were three SEP events detected either at Earth or at one of the two STEREO spacecraft when K‐Cor was observing during the relevant time period. The algorithm successfully generated alerts for two of these events, with lead times of 1–3 h before the SEP onset at 1 AU. The third event was not detected by the automatic algorithm because of the unusually broad width in position angle.

The Significance of the Influence of the CME Deflection in Interplanetary Space on the CME Arrival at Earth

Abstract As one of the most violent astrophysical phenomena, coronal mass ejections (CMEs) have strong potential space weather effects. However, not all Earth-directed CMEs encounter the Earth and produce geo-effects. One reason is the deflected propagation of CMEs in interplanetary space. Although there have been several case studies clearly showing such deflections, it has not yet been statistically assessed how significantly the deflected propagation would influence the CME’s arrival at Earth. We develop an integrated CME-arrival forecasting (iCAF) system, assembling the modules of CME detection, three-dimensional (3D) parameter derivation, and trajectory reconstruction to predict whether or not a CME arrives at Earth, and we assess the deflection influence on the CME-arrival forecasting. The performance of iCAF is tested by comparing the two-dimensional (2D) parameters with those in the Coordinated Data Analysis Workshop (CDAW) Data Center catalog, comparing the 3D parameters with those of the gradual cylindrical shell model, and estimating the success rate of the CME Earth-arrival predictions. It is found that the 2D parameters provided by iCAF and the CDAW catalog are consistent with each other, and the 3D parameters derived by the ice cream cone model based on single-view observations are acceptable. The success rate of the CME-arrival predictions by iCAF with deflection considered is about 82%, which is 19% higher than that without deflection, indicating the importance of the CME deflection for providing a reliable forecasting. Furthermore, iCAF is a worthwhile project since it is a completely automatic system with deflection taken into account.

Visibility of coronal mass ejections in SOHO/LASCO coronagraphs

We studied the detection efficiency of coronal mass ejections (CMEs) of the Large Angle Spectrometric Coronagraph (LASCO) on board the Solar and Heliospheric Observatory (SOHO). For this purpose LASCO/SOHO observations are compared with these obtained by the two Solar Terrestrial Relations Observatory (STEREO) satellites in quadrature in the period of time June-November 2011. These unprecedented observations enable us to the direct detection of CMEs that are not visible in LASCO coronagraphs (invisible events). Determination of these events allowed us to evaluate the detection efficiency of LASCO coronagraphs. We found that the total visibility function is ≈0.80. Having source location, from associated flares or other signatures observed in the corona, longitudinal variation of the visibility function was also found. It was demonstrated that invisible-to-LASCO CMEs are narrow (average width is only 20°), slow (average velocity is 328kms−1) and originate from the disk center. We have shown that the detection efficiency of the LASCO coronagraphs with typical data availability is sufficient to detect all potentially geoeffective CMEs.

The Inner Coronagraph on Board ADITYA-L1 and Automatic Detection of CMEs

AbstractVisible Emission Line Coronagraph (VELC) on board ADITYA-L1 is an internally occulted coronagraph with mirror as its primary objective element. It has a field of view (FOV) starting from 1.05 R⊙– 3 R⊙. It will observe the corona in continuum centered at 5000 Å and will perform spectroscopic observations of inner corona in two visible (5303 Å and 7892 Å) and one infrared (10747 Å) wavelengths. VELC will be capable of observingthecorona with high spatial and temporal resolutions. We present an overview of the inner coronagraph (VELC) design and introduce the concept of an on-board automated coronal mass ejections (CMEs) detection logic proposed for this payload.

Detection of Coronal Mass Ejections Using Multiple Features and Space–Time Continuity

Coronal Mass Ejections (CMEs) release tremendous amounts of energy in the solar system, which has an impact on satellites, power facilities and wireless transmission. To effectively detect a CME in Large Angle Spectrometric Coronagraph (LASCO) C2 images, we propose a novel algorithm to locate the suspected CME regions, using the Extreme Learning Machine (ELM) method and taking into account the features of the grayscale and the texture. Furthermore, space–time continuity is used in the detection algorithm to exclude the false CME regions. The algorithm includes three steps: i) define the feature vector which contains textural and grayscale features of a running difference image; ii) design the detection algorithm based on the ELM method according to the feature vector; iii) improve the detection accuracy rate by using the decision rule of the space–time continuum. Experimental results show the efficiency and the superiority of the proposed algorithm in the detection of CMEs compared with other traditional methods. In addition, our algorithm is insensitive to most noise.

Coronal Mass Ejections detection using multiple features based ensemble learning

Coronal Mass Ejection (CME) is a major solar activity that affects the earth, thus CMEs detection is of great importance for space weather forecast, disaster prevention and reduction. We model the detection of Coronal Mass Ejections (CMEs) as the classification of the brightest block in the current running difference image. Because CMEs usually correspond to the areas with high gray values or complex texture features, multiple features including gray features and texture features are extracted to represent the brightest block. And classifier is designed based on these features. Our method includes four steps: first, because the CMEs spread along the radial direction of the sun, in order to facilitate the analysis, the original coordinate is transformed into the polar coordinate; Secondly, because the typical appearance of the CMEs is bright or complex texture enhancement, we use the brightest block to represent the whole image; Thirdly, we extract the gray, texture and HOG features of the brightest gray blocks. Finally, we use the extracted features to design decision trees as the base classifiers, and AdaBoost is used to obtain the final ensemble classifier. As far as we know, this is the first time that the learning based classification framework is presented in the CMEs detection. Moreover, multiple feature fusion is first used to model the various CMEs. Experimental results show that the integration of multi-feature based detection algorithm proposed can achieve better detection results.

An end-to-end method of Coronal Mass Ejections detection

Coronal Mass Ejections (CMEs) impact heavily on coronal activity, space weather and many interplanetary disturbance, so the detection of CMEs are important for space weather disaster prevention and reduction. The traditional methods use man-made features or predefined threshold to solve this problem. Despite the great progress in the detection of CMEs, it is still a challenging problem due to the following three aspects: Firstly, the early and late stage of the CMEs phenomenon is very weak, and the traditional image processing based method can not detect this weak CMEs well. Secondly, the noises from comet, planets and other stars can affect the detection of CMEs. Thirdly, the CMEs are complex and amorphous, and they are different in shapes, textures, grayscales, scales and so on. Because of these difficulties, it is difficult to detect CMEs well by the traditional image processing method without modeling the CMEs. With the development of convolutional neural networks (CNNs), it is possible to develop deep neural networks based CMEs detection models to better solve this problem. For realizing this, this paper presents an end-to-end detection method of Coronal Mass Ejections detection: We design a deep neural network with 4 convolution layers, 1 full connection layer and 1 output layer. This deep neural network can automatically extract the image features that are suitable to describe the Coronal Mass Ejections, and can establish the CMEs detection model based on the extracted features. In order to achieve good performance, we construct two datasets, one is mainly made up of strong CMEs, and the other is made up of weak or dark CMEs. Training is first done on the strong CMEs to obtain the initial CMEs detection model. Based on the initial model established on the strong CMEs, finetuning is used on the weak CMEs to acquire the final CMEs detection model. By using this scheme, training efficiency and good performance can be guaranteed. In addition, the process is able to achieve selection of features and setting of classification rules, which can realize conveniently the end-to-end detection from data to results. The experimental results show that this method can effectively detect CMEs. The accuracy of our method is 100% on the strong CME dataset, and 91.54% on the weak CME dataset. The overall accuracy on our dataset is 98.05%. Finally, we test our method with the real data on May 2007 using LASCO catalog as the groudtruth. Experimental results show that our method achieves state-of-the art performance comparing with the usually used catalog, SEEDS, CACTUS. The significance of our work is two-fold. On one hand, our method is end-to-end method which can select optimal features for the detection task. According to the theory of deep neural networks, the selected features are usually those that the human vision uses. Therefore, the obtained features can depicts CMEs well and can be used by other methods. On the other hand, the good performance of our method proves the depiction ability of the CNN for CME. So it is natural that CNN based description for the CME can also have good performance for the study of the modeling of the evolution of the CMEs.

Automated detection of coronal mass ejections in three-dimensions using multi-viewpoint observations

A new, automated method of detecting coronal mass ejections (CMEs) in three dimensions for the LASCO C2 and STEREO COR2 coronagraphs is presented. By triangulating isolated CME signal from the three coronagraphs over a sliding window of five hours, the most likely region through which CMEs pass at 5 solar radii is identified. The centre and size of the region gives the most likely direction of propagation and approximate angular extent. The Automated CME Triangulation (ACT) method is tested extensively using a series of synthetic CME images created using a wireframe flux rope density model, and on a sample of real coronagraph data; including halo CMEs. The accuracy of the angular difference between the detection and true input of the synthetic CMEs is 7.14 degrees, and remains acceptable for a broad range of CME positions relative to the observer, the relative separation of the three observers and even through the loss of one coronagraph. For real data, the method gives results that compare well with the distribution of low coronal sources and results from another instrument and technique made further from the Sun. The true three dimension (3D)-corrected kinematics and mass/density are discussed. The results of the new method will be incorporated into the CORIMP database in the near future, enabling improved space weather diagnostics and forecasting.

Comparing Automatic CME Detections in Multiple LASCO and SECCHI Catalogs

Abstract With the creation of numerous automatic detection algorithms, a number of different catalogs of coronal mass ejections (CMEs) spanning the entirety of the Solar and Heliospheric Observatory (SOHO) Large Angle Spectrometric Coronagraph (LASCO) mission have been created. Some of these catalogs have been further expanded for use on data from the Solar Terrestrial Earth Observatory (STEREO) Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) as well. We compare the results from different automatic detection catalogs (Solar Eruption Event Detection System (SEEDS), Computer Aided CME Tracking (CACTus), and Coronal Image Processing (CORIMP)) to ensure the consistency of detections in each. Over the entire span of the LASCO catalogs, the automatic catalogs are well correlated with one another, to a level greater than 0.88. Focusing on just periods of higher activity, these correlations remain above 0.7. We establish the difficulty in comparing detections over the course of LASCO observations due to the change in the instrument image cadence in 2010. Without adjusting catalogs for the cadence, CME detection rates show a large spike in cycle 24, despite a notable drop in other indices of solar activity. The output from SEEDS, using a consistent image cadence, shows that the CME rate has not significantly changed relative to sunspot number in cycle 24. These data, and mass calculations from CORIMP, lead us to conclude that any apparent increase in CME rate is a result of the change in cadence. We study detection characteristics of CMEs, discussing potential physical changes in events between cycles 23 and 24. We establish that, for detected CMEs, physical parameters can also be sensitive to the cadence.

AUTOMATED DETECTION OF CORONAL MASS EJECTIONS IN STEREO HELIOSPHERIC IMAGER DATA

ABSTRACT We have performed, for the first time, the successful automated detection of coronal mass ejections (CMEs) in data from the inner heliospheric imager (HI-1) cameras on the STEREO-A spacecraft. Detection of CMEs is done in time–height maps based on the application of the Hough transform, using a modified version of the CACTus software package, conventionally applied to coronagraph data. In this paper, we describe the method of detection. We present the results of the application of the technique to a few CMEs, which are well detected in the HI-1 imagery, and compare these results with those based on manual-cataloging methodologies. We discuss, in detail, the advantages and disadvantages of this method.