Sunspot Classification Research Articles

Analysis of Flare Occurring Based on Sunspot Changing Patterns Using Zurich Method in February – April 2022 at the Astronomy Laboratory of Physics FMIPA State University of Malang

The star closest to our Earth is the sun with an average distance of 149,680,000 km. The sun is plasma with a very high temperature. The most easily observed solar activity from Earth are sunspots and flares, where flares on the sun can affect the Earth’s atmosphere. Therefore with this, research was carried out in February - April 2022 at the Laboratory of Astronomy Physics, State University of Malang with a geographical location of 07° 57’ 39” South Latitude, 112° 37’ 9.2” East Longitude, and 7.9607° North Latitude at an altitude of 465 meters with using the Celestron telescope. This study aims to determine the pattern of changes in sunspots until the occurrence of flare, and to find out how to determine the occurrence of flare. This study used a descriptive approach based on the daily data that had been observed. In classifying sunspots, the Zurich method is used. The development of sunspots obtained in daily data shows changes in sunspot development patterns and data on the influence of flares that occur on the surface of the Earth such as satellite damage and auroras reported by NASA show that flare occur on the surface of the sun. Based on the analysis of sunspot classification, it shows that flares occur in classes C, D, E, and F. The occurrence of these flares can be seen in the news released by NOAA. It can be concluded that flares do not always occur in large class sunspot classifications. However, the small class was able to produce a flare.

Detection and classification of sunspots via deep convolutional neural network

Sunspots are known to be the most prominent feature of the solar photosphere. Solar activities play a vital role in Space weather which greatly affects the Earth's environment. The appearance of sunspots determines the solar activities and being observed from early eighteenth century. In this work, we have implemented a deep learning model which automatically detects sunspots from MDI and HMI image datasets. Proposed model uses Alexnet based deep convolutional networks to generate promising deep hierarchical features and proposed deep learning approach achieved excellent classification accuracies. Also, model has shown the improved result with MDI data set which is equal to 99.71%, 100%, 100%, and 100 for accuracy, precision, recall, and F-score respectively. This is to construct and build robust and reliable event recognition system to monitor solar activities which are crucial to understanding space weather and for physicists it is an aid for their research.

Chinese sunspot drawings and their digitization – (II) accuracy analysis for digitized sunspot hand-drawing records of Yunnan Observatories

Sunspots are the most striking and easily observed magnetic structures of the Sun, and statistical analysis of solar historical data could reveal a wealth of information on the long-term variation of solar activity cycle. The hand-drawn sunspot records of Yunnan Observatories, Chinese Academy of Sciences have been accumulating for more than 60 years, and nearly 16 000 images have been preserved. In the future, the observation mode of recording sunspots by hand-drawing will be replaced inevitably by digital images observed either at ground or in space. To connect the hand-drawn sunspot data and the purely digital sunspot data in future, it is necessary to analyze the systematic errors of the data which are observed by the two observation modes in the period of transition. In this paper, we choose 268 round sunspots (H-type in modified Zurich sunspot classification) from the drawing of Yunnan Observatories to compare their positions and areas with the CCD observations made by Helioseismic and Magnetic Imager (HMI) on board Solar Dynamic Observatory (SDO) and Global Oscillation Network Group (GONG). We find that the latitude and longitude accuracy of hand-drawn sunspot are within −0.127 and 2.29 degree respectively, and the area accuracy is about 16.36 sunspot unit (μHem). Systematic errors apparently decrease with large sunspot.

Connection between Active region complexity and Solar Flare strength

AbstractHale (1908) discovered the existence of magnetic fields in sunspots, and since then a consensus has been reached that magnetic fields play an important role in various forms of solar activities, such as solar flares . Modified Mount-Wilson scheme is one of the methodology to classify active regions based on their complexity . As per this scheme, sunspots are classified as α, β, γ, and δ with the complexity of the magnetic topology increasing from α to δ. The δ sunspots are known to be highly flare-productive. An existing automated algorithm (SMART-DF) is modified and used to identify δ-spots for the existing full disk SOHO/MDI data. The automatically identified δ-spots is compared with the NOAA-SRS database and found to be reproducing almost all the identified δ-spots. In thisstudy, the connection between formation of δ-spot and flares is also carried out using GOES flare flux and NOAA-SRS sunspot classification.

An Approach to Pattern Recognition Based on Hierarchical Granular Computing

This paper summarizes the some of the recent developments in the area of application of rough sets and granular computing in hierarchical learning. We present the general framework of rough set based hierarchical learning. In particular, we investigate several strategies of choosing the appropriate learning algorithms for first level concepts as well as the learning methods for the intermediate concepts. We also propose some techniques for embedding the domain knowledge into the granular, layered learning process in order to improve the quality of hierarchical classifiers. This idea, which has been envisioned and developed by professor Andrzej Skowron over the last 10 years, shows to be very efficient in many practical applications. Throughout the article, we illustrate the proposed methodology with three case studies in the area of pattern recognition. The studies demonstrate the viability of this approach for such problems as: sunspot classification, hand-written digit recognition, and car identification.

Solar Flare Occurrence Rate and Probability in Terms of the Sunspot Classification Supplemented with Sunspot Area and Its Changes

We investigate the solar flare occurrence rate and daily flare probability in terms of the sunspot classification supplemented with sunspot area and its changes. For this we use the NOAA active region data and GOES solar flare data for 15 years (from January 1996 to December 2010). We consider the most flare-productive 11 sunspot classes in the McIntosh sunspot group classification. Sunspot area and its changes can be a proxy of magnetic flux and its emergence/cancellation, respectively. We classify each sunspot group into two sub-groups by its area: “Large” and “Small”. In addition, for each group, we classify it into three sub-groups according to sunspot area changes: “Decrease”, “Steady”, and “Increase”. As a result, in the case of compact groups, their flare occurrence rates and daily flare probabilities noticeably increase with sunspot group area. We also find that the flare occurrence rates and daily flare probabilities for the “Increase” sub-groups are noticeably higher than those for the other sub-groups. In case of the (M+X)-class flares in the ‘Dkc’ group, the flare occurrence rate of the “Increase” sub-group is three times higher than that of the “Steady” sub-group. The mean flare occurrence rates and flare probabilities for all sunspot groups increase with the following order: “Decrease”, “Steady”, and “Increase”. Our results statistically demonstrate that magnetic flux and its emergence enhance the occurrence of major solar flares.

Automated Solar Activity Prediction: A hybrid computer platform using machine learning and solar imaging for automated prediction of solar flares

The importance of real‐time processing of solar data especially for space weather applications is increasing continuously. In this paper, we present an automated hybrid computer platform for the short‐term prediction of significant solar flares using SOHO/Michelson Doppler Imager images. This platform is called the Automated Solar Activity Prediction tool (ASAP). This system integrates image processing and machine learning to deliver these predictions. A machine learning‐based system is designed to analyze years of sunspot and flare data to create associations that can be represented using computer‐based learning rules. An imaging‐based real‐time system that provides automated detection, grouping, and then classification of recent sunspots based on the McIntosh classification is also created and integrated within this system. The properties of the sunspot regions are extracted automatically by the imaging system and processed using the machine learning rules to generate the real‐time predictions. Several performance measurement criteria are used and the results are provided in this paper. Also, quadratic score is used to compare the prediction results of ASAP with NOAA Space Weather Prediction Center (SWPC) between 1999 and 2002, and it is shown that ASAP generates more accurate predictions compared to SWPC.

Automatic Sunspots Detection on Full-Disk Solar Images using Mathematical Morphology

Sunspots are solar features located in active regions of the Sun, whose number is an indicator of the Sun’s magnetic activity. Therefore accurate detection and classification of sunspots are fundamental for the elaboration of solar activity indices such as the Wolf number. However, irregularities in the shape of the sunspots and their variable intensity and contrast with the surroundings, make their automated detection from digital images difficult. Here, we present a morphological tool that has allowed us to construct a simple and automatic procedure to treat digital photographs obtained from a solar telescope, and to extract the main features of sunspots. Comparing the solar indices computed with our algorithm against those obtained with the previous method exhibit an obvious improvement. A favorable comparison of the Wolf sunspot number time series obtained with our methodology and from other reference observatories is also presented. Finally, we compare our sunspot and group detection to that of other observatories.

Automated McIntosh-Based Classification of Sunspot Groups Using MDI Images

This paper presents a hybrid system for automatic detection and McIntosh-based classification of sunspot groups on SOHO/MDI white-light images using active-region data extracted from SOHO/MDI magnetogram images. After sunspots are detected from MDI white-light images they are grouped/clustered using MDI magnetogram images. By integrating image-processing and neural network techniques, detected sunspot regions are classified automatically according to the McIntosh classification system. Our results show that the automated grouping and classification of sunspots is possible with a high success rate when compared to the existing manually created catalogues. In addition, our system can detect and classify sunspot groups in their early stages, which are usually missed by human observers.

Learning Sunspot Classification

Sunspots are the subject of interest to many astronomers and solar physicists. Sunspot observation, analysis and classification form an important part of furthering the knowledge about the Sun. Sunspot classification is a manual and very labor intensive process that could be automated if successfully learned by a machine. This paper presents machine learning approaches to the problem of sunspot classification. The classification scheme attempted was the seven-class Modified Zurich scheme [18]. The data was obtained by processing NASA SOHO/MDI satellite images to extract individual sunspots and their attributes. A series of experiments were performed on the training dataset with an aim of learning sunspot classification and improving prediction accuracy. The experiments involved using decision trees, rough sets, hierarchical clustering and layered learning methods. Sunspots were characterized by their visual properties like size, shape, positions, and were manually classified by comparing extracted sunspots with corresponding active region maps (ARMaps) from the Mees Observatory at the Institute for Astronomy, University of Hawaii.

A Bayesian Approach to Solar Flare Prediction

A number of methods of flare prediction rely on classification of physical characteristics of an active region, in particular optical classification of sunspots, and historical rates of flaring for a given classification. However, these methods largely ignore the number of flares the active region has already produced, in particular the number of small events. The past history of occurrence of flares (of all sizes) is an important indicator of future flare production. We present a Bayesian approach to flare prediction, which uses the flaring record of an active region together with phenomenological rules of flare statistics to refine an initial prediction for the occurrence of a big flare during a subsequent period of time. The initial prediction is assumed to come from one of the extant methods of flare prediction. The theory of the method is outlined, and simulations are presented to show how the refinement step of the method works in practice.

Solar image segmentation by use of mean field fast annealing

We present a continuous” analysis of a solar image in order to address the problem of image segmentation. Our approach is based on combinatorial optimization methods and in particular on Mean Field Fast Annealing (MFFA). Mean-field theory gives a deterministic nature to our algorithm while its efficiency is improved by a fast cooling schedule. We show how this method can be used to separate efficiently the regions of different solar activity giving a tool for a future automated recognition and classification of sunspots.

The classification of sunspot groups

The 3-component McIntosh classification of sunspots was introduced in 1966, adopted for interchange and publication of data in 1969, and has been used increasingly in recent years. The McIntosh classification uses a modified Zurich evolutionary sequence as its first component, class, where two of the Zurich classes are omitted and more quantitative definitions are used. It then adds descriptions of the largest spot (second component) and the degree of spottedness in the group interior (third component) to define 60 distinct types of sunspot groups. Definitions of the McIntosh classification system and their rationale are presented herein. Correlations with solar flares excel those with the earlier Zurich classification, prompting the use of the McIntosh classification in an expert system (Theo) for predicting X-ray solar flares.

THE MAGNETIC CLASSIFICATION OF SUN-SPOTS ASSOCIATED WITH TERESTRIAL MAGNETIC STARS (ABSTRACT)

THE MAGNETIC CLASSIFICATION OF SUN-SPOTS ASSOCIATED WITH TERESTRIAL MAGNETIC STARS (ABSTRACT)