Heliophysics Event Knowledgebase Research Articles

Solar Jet Hunter: a citizen science initiative to identify and characterize coronal jets at 304 AA

Solar coronal jets seen in extreme ultraviolet (EUV) are ubiquitous on the Sun, and they have been found in and at the edges of active regions, at the boundaries of coronal holes, and in the quiet Sun. Jets have various shapes, sizes, brightness, velocities, and durations in time, which complicates their detection by automated algorithms. So far, solar jets reported in the Heliophysics Event Knowledgebase (HEK) have been mostly reported by humans looking for them in the data, with different levels of precision regarding their timing and positions. We created a catalog of solar jets observed in EUV at 304 \ containing precise and consistent information on the jet timing, position, and extent. We designed a citizen science project, Solar Jet Hunter, on the Zooniverse platform, to analyze EUV observations at 304 \ from the Solar Dynamic Observatory/Atmospheric Imaging Assembly (SDO/AIA). We created movie strips for regions of the Sun in which jets have been reported in HEK and ask the volunteers to 1) confirm the presence of at least one jet in the data and 2) report the timing, position, and extent of the jet. We report here the design of the project and the results obtained after the analysis of data from 2011 to 2016. We note that 365 "coronal jet" events from HEK served as input for the citizen science project, equivalent to more than 120,000 images distributed into 9,689 "movie strips." Classification by the citizen scientists resulted in 883 individual jets being identified. We demonstrate how citizen science can enhance the analysis of solar data with the example of Solar Jet Hunter. The catalog of jets thusly created is publicly available and will enable statistical studies of jets and related phenomena. This catalog will also be used as a training set for machines to learn to recognize jets in further datasets.

Evolutionary features of polar and non-polar coronal holes during solar cycle 24 and the rising phase of cycle 25

This paper presents the results of the analysis of the evolution of coronal holes (CHs) on the Sun during the period May 13, 2010 – March 20, 2022, covering Solar Cycle 24. Our study uses images in the extreme-ultraviolet iron line (Fe XII 193 Å) obtained with the Atmospheric Imager Assembly of the Solar Dynamics Observatory (AIA/SDO). To localize CHs and determine their areas, we used the Heliophysics Event Knowledgebase (HEK). We separate the CHs into polar and non-polar and study the evolutionary features of each group. During this period, an asymmetry between the Northern (N) and Southern (S) Hemispheres (N-S or hemispheric asymmetry) is detected both in the solar activity (SA) indices and in the localization of the maximum areas of the polar and non-polar CHs. It is shown that the hemispheric asymmetry of the areas of polar and non-polar CHs varies significantly over time and that the nature of these changes is clearly related to the SA cycle. We find that for most of the period, the polar CHs were predominated generated in the S- hemisphere while the non-polar CHs were dominant in the N- hemisphere. It is found that the maximum and minimum of the hemispheric imbalance in the areas of non-polar CHs are close in time to the maximum and minimum of the asymmetry of the SA indices (the number and areas of sunspots). The maximum hemispheric imbalance of the polar CH areas is observed at the maximum of Cycle 24, and the minimum imbalance is found at the cycle minimum. These results confirm our assumption that these two types of CHs are of a different nature and that the non-polar CHs, like sunspots, are elements of the general magnetic activity.

11-year dynamics of coronal hole and sunspot areas

Abstract The paper presents study the 11-year dynamics of solar activity on the basis of new observational material on coronal holes (CHs) and sunspots in the period from May 13, 2010 to May 13, 2021. We used the Heliophysics Event Knowledgebase (HEK) to obtain information on CHs areas. For 11 years of observations, we processed about 18000 CHs. Slightly more than 8000 are polar, the rest are nonpolar CHs. The statistical volume of the presented material is quite extensive and gives grounds for the study of the dynamics of different types of CHs during the cycle. Our research has shown: in the 24th solar activity cycle, the South led for polar CHs and the North led for nonpolar ones. We established a relationship between the number and area of CHs and the phase of the solar cycle. The number and daily total area of polar CHs increases at the minima of solar activity and decreases at the maximum of the cycle. This is consistent with the general concept of polar CHs as the main source of the solar dipole magnetic field. An asymmetry in both the number and areas of polar coronal holes in the northern and southern hemispheres is observed. It is shown that the areas of nonpolar CHs change quasi-synchronously with sunspot activity, which suggests a physical connection between these two phenomena.

Effects of flares on solar high-degree helioseismic acoustic mode amplitudes

ABSTRACT Several attempts have been made to observe whether solar flares excite acoustic modes since Charles Wolff suggested this possibility almost 50 yr ago. We look for the impact of flares on the amplitude of solar acoustic modes and other effects that are also affecting the mode amplitude. Solar acoustic mode amplitudes are known to be sensitive to magnetic fields. As flares usually occur in the presence of strong magnetic fields and most likely are the by-product of magnetic reconnection, we show how the magnetic field in and around the flaring region affects the mode amplitude. The mode amplitudes were obtained using ring-diagram analysis, which was first applied to a single event, the largest flare in the space age (the ‘Halloween Flare’, SOL2003-10-28T11:00), using MDI (Michelson Doppler Imager) data. Then, using HMI (Helioseismic and Magnetic Imager) data, the analysis was applied to the regions corresponding to the flares observed during the high-activity phase of cycle 24 and that fall into two groups. These two groups consist of small (10–60 erg cm−2 s−1) and large (>1200 erg cm−2 s−1) peak-flux flares, based on the Heliophysics Event Knowledgebase (HEK). The first set is used as a comparison to the results of the strong flares in the second set. After applying several corrections in order to take into account several sources of bias, we did not find any amplification in the inferred mode amplitude due to flaring activity larger than a 10 per cent uncertainty.

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.

Tracking Solar Phenomena from the SDO

Abstract This paper focuses on the problem of tracking solar phenomena by creating spatiotemporal trajectories from solar event detection reports. Though tracking of multiple objects in video sequences has seen much research and improvement in recent years, there has been relatively little focus on the domain of tracking solar phenomena (events). In this work, we improve on our previous endeavors by eliminating offline model training requirements and utilizing crowd-sourced human labels to evaluate our performance. We apply our method to the metadata of two solar event types spanning 4 yr of detection reports from the automated detection modules for the Solar Dynamics Observatory mission. We compare our results with those produced by the detection module for active regions and coronal holes by using a crowd-sourced trajectory database as the ground truth. We show that our results are as good as or better than the event-specific detection module for these two event types. This is especially promising because our tracking algorithm is a generalized module for all solar events, and not specific to a single event type, allowing it to be applied to other solar event types reported to the Heliophysics Event Knowledgebase that do not contain tracking information.

Identification of Solar Activities Based on Heliophysics Event Knowledgebase

Solar image consists of obvious regions of solar activities and peaceful regions of no activity. Identifying effective solar active areas from solar image is a typical application of image-processing technology in astronomical research. Benefiting from real-time solar observation data provided by Heliophysics Event Knowledgebase (HEK) of Solar Dynamics Observatory (SDO), this paper presents a method of solar activity identification based on HEK. In this method, we gather data of six kinds of solar activities (time, location, and area) and establish a multiscale transformation model for the full-disk solar image of the corresponding time. Combining both location data and area data, we design different gradient thresholds to segment different solar activities’ regions. Two kinds of boundary-identification methods are used to locate and recognize solar activities’ regions, respectively. Furthermore, we study the correlation of the characteristic parameters (six kinds of solar activities’ data) of solar image and get the best combination of the characteristic parameters of each solar activity. The method in the paper provides precise positioning and efficient identification of solar activities, which lays the foundation for further work. In addition to that, extracting specific combination of features from different solar activities’ regions can provide a feasible way to build a streamlined image feature set for content-based image retrieval (CBIR) systems.

Prediction of Solar Eruptions Using Filament Metadata

Abstract We perform a statistical analysis of erupting and non-erupting solar filaments to determine the properties related to the eruption potential. In order to perform this study, we correlate filament eruptions documented in the Heliophysics Event Knowledgebase (HEK) with HEK filaments that have been grouped together using a spatiotemporal tracking algorithm. The HEK provides metadata about each filament instance, including values for length, area, tilt, and chirality. We add additional metadata properties such as the distance from the nearest active region and the magnetic field decay index. We compare trends in the metadata from erupting and non-erupting filament tracks to discover which properties present signs of an eruption. We find that a change in filament length over time is the most important factor in discriminating between erupting and non-erupting filament tracks, with erupting tracks being more likely to have decreasing length. We attempt to find an ensemble of predictive filament metadata using a Random Forest Classifier approach, but find the probability of correctly predicting an eruption with the current metadata is only slightly better than chance.

A large-scale dataset of solar event reports from automated feature recognition modules

The massive repository of images of the Sun captured by the Solar Dynamics Observatory (SDO) mission has ushered in the era of Big Data for Solar Physics. In this work, we investigate the entire public collection of events reported to the Heliophysics Event Knowledgebase (HEK) from automated solar feature recognition modules operated by the SDO Feature Finding Team (FFT). With the SDO mission recently surpassing five years of operations, and over 280,000 event reports for seven types of solar phenomena, we present the broadest and most comprehensive large-scale dataset of the SDO FFT modules to date. We also present numerous statistics on these modules, providing valuable contextual information for better understanding and validating of the individual event reports and the entire dataset as a whole. After extensive data cleaning through exploratory data analysis, we highlight several opportunities for knowledge discovery from data (KDD). Through these important prerequisite analyses presented here, the results of KDD from Solar Big Data will be overall more reliable and better understood. As the SDO mission remains operational over the coming years, these datasets will continue to grow in size and value. Future versions of this dataset will be analyzed in the general framework established in this work and maintained publicly online for easy access by the community.

Tracking Solar Events through Iterative Refinement

Abstract In this paper, we combine two approaches to multiple-target tracking: the first is a hierarchical approach to iteratively growing track fragments across gaps in detections; the second is a network flow based optimization method for data association. The network flow based optimization method is utilized for data association in an iteratively growing manner. This process is applied to solar data, retrieved from the Heliophysics Event Knowledge base (HEK) and utilizes precomputed image parameter values. These precomputed image parameter values are used to compare visual similarity of detected events, to determine the best matching track fragment associations, which leads to a globally optimal track fragment association hypothesis.

SunPy—Python for solar physics

This paper presents SunPy (version 0.5), a community-developed Python package for solar physics. Python, a free, cross-platform, general-purpose, high-level programming language, has seen widespread adoption among the scientific community, resulting in the availability of a large number of software packages, from numerical computation (NumPy, SciPy) and machine learning (scikit-learn) to visualisation and plotting (matplotlib). SunPy is a data-analysis environment specialising in providing the software necessary to analyse solar and heliospheric data in Python. SunPy is open-source software (BSD licence) and has an open and transparent development workflow that anyone can contribute to. SunPy provides access to solar data through integration with the Virtual Solar Observatory (VSO), the Heliophysics Event Knowledgebase (HEK), and the HELiophysics Integrated Observatory (HELIO) webservices. It currently supports image data from major solar missions (e.g., SDO, SOHO, STEREO, and IRIS), time-series data from missions such as GOES, SDO/EVE, and PROBA2/LYRA, and radio spectra from e-Callisto and STEREO/SWAVES. We describe SunPy's functionality, provide examples of solar data analysis in SunPy, and show how Python-based solar data-analysis can leverage the many existing tools already available in Python. We discuss the future goals of the project and encourage interested users to become involved in the planning and development of SunPy.

Prominence and Filament Eruptions Observed by the Solar Dynamics Observatory: Statistical Properties, Kinematics, and Online Catalog

We present a statistical study of prominence and filament eruptions observed by the Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamics Observatory (SDO). Several properties are recorded for 904 events that were culled from the Heliophysics Event Knowledgebase (HEK) and incorporated into an online catalog for general use. These characteristics include the filament and eruption type, eruption symmetry and direction, apparent twisting and writhing motions, and the presence of vertical threads and coronal cavities. Associated flares and white-light coronal mass ejections (CME) are also recorded. Total rates are given for each property along with how they differ among filament types. We also examine the kinematics of 106 limb events to characterize the distinct slow- and fast-rise phases often exhibited by filament eruptions. The average fast-rise onset height, slow-rise duration, slow-rise velocity, maximum field-of-view (FOV) velocity, and maximum FOV acceleration are 83 Mm, 4.4 hours, 2.1 km/s, 106 km/s, and 111 m/s^2, respectively. All parameters exhibit lognormal probability distributions similar to that of CME speeds. A positive correlation between latitude and fast-rise onset height is found, which we attribute to a corresponding negative correlation in the average vertical magnetic field gradient, or decay index, estimated from potential field source surface (PFSS) extrapolations. We also find the decay index at the fast-rise onset point to be 1.1 on average, consistent with the critical instability threshold theorized for straight current channels. Finally, we explore relationships between the derived kinematics properties and apparent twisting motions. We find that events with evident twist have significantly faster CME speeds and significantly lower fast-rise onset heights, suggesting relationships between these values and flux rope helicity.

Automated detection of solar eruptions

Observation of the solar atmosphere reveals a wide range of motions, from small scale jets and spicules to global-scale coronal mass ejections. Identifying and characterizing these motions are essential to advancing our understanding the drivers of space weather. Both automated and visual identifications are currently used in identifying CMEs. To date, eruptions near the solar surface (which may be precursors to CMEs) have been identified primarily by visual inspection. Here we report on EruptionPatrol (EP): a software module that is designed to automatically identify eruptions from data collected by SDO/AIA. We describe the method underlying the module and compare its results to previous identifications found in the Heliophysics Event Knowledgebase. EP identifies eruptions events that are consistent with those found by human annotations, but in a significantly more consistent and quantitative manner. Eruptions are found to be distributed within 15Mm of the solar surface. They possess peak speeds ranging from 4 to 100 km/sec and display a power-law probability distribution over that range. These characteristics are consistent with previous observations of prominences.

CorPITA: An Automated Algorithm for the Identification and Analysis of Coronal “EIT Waves”

The continuous stream of data available from the Atmospheric Imaging Assembly (AIA) telescopes onboard the Solar Dynamics Observatory (SDO) spacecraft has allowed a deeper understanding of the Sun. However, the sheer volume of data has necessitated the development of automated techniques to identify and analyse various phenomena. In this article, we describe the Coronal Pulse Identification and Tracking Algorithm (CorPITA) for the identification and analysis of coronal “EIT waves”. CorPITA uses an intensity-profile technique to identify the propagating pulse, tracking it throughout its evolution before returning estimates of its kinematics. The algorithm is applied here to a data set from February 2011, allowing its capabilities to be examined and critiqued. This algorithm forms part of the SDO Feature Finding Team initiative and will be implemented as part of the Heliophysics Event Knowledgebase (HEK). This is the first fully automated algorithm to identify and track the propagating “EIT wave” rather than any associated phenomenon and will allow a deeper understanding of this controversial phenomenon.