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.     

紫金山天文台55 yr手绘黑子数据系统差分析

对紫金山天文台(简称紫台)自1954年至2011年共55 yr的手描黑子图进行了数字化.将紫台太阳黑子相对数(PRSN)和黑子群数(PGSN)与国际太阳影响数据分析中心(SIDC)中的对应数据(月平均太阳黑子相对数(IRSN)和月平均黑子群数(IGSN))进行对比研究,发现:(1)紫台黑子数据与SIDC黑子数据有很强的正相关性,说明紫台黑子数据的可靠性;(2) PRSN和IRSN、PGSN和IGSN的系统偏差分别处于7%左右、5%左右,紫台数据与SIDC数据在活动周的极小期的差异性显著大于极大期;(3)紫台的视宁度从1995年开始变差,直接导致了PRSN (PGSN)与IRSN (IGSN)的比值明显变大,表明视宁度的变化影响了紫台黑子的观测质量.     

AR8210大活动区黑子与磁场的演化

AR8 2 1 0活动区的黑子磁场结构是反极性排列 ,开始是负极性的主黑子上半部被正极性所包围 ,随后又在主黑子下方浮现正极磁场 ,引起主黑子作顺时针方向旋转约 90°,当正极性磁场强度减弱后 ,主黑子又呈弱的逆时针方向旋转。该区域产生的高能耀斑爆发与黑子磁场变化密切联系。     

A Multi-wavelength Analysis of Active Regions and Sunspots by Comparison of Automatic Detection Algorithms

Since the Solar Dynamics Observatory (SDO) began recording ≈?1 TB of data per day, there has been an increased need to automatically extract features and events for further analysis. Here we compare the overall detection performance, correlations between extracted properties, and usability for feature tracking of four solar feature-detection algorithms: the Solar Monitor Active Region Tracker (SMART) detects active regions in line-of-sight magnetograms; the Automated Solar Activity Prediction code (ASAP) detects sunspots and pores in white-light continuum images; the Sunspot Tracking And Recognition Algorithm (STARA) detects sunspots in white-light continuum images; the Spatial Possibilistic Clustering Algorithm (SPoCA) automatically segments solar EUV images into active regions (AR), coronal holes (CH), and quiet Sun (QS). One month of data from the Solar and Heliospheric Observatory (SOHO)/Michelson Doppler Imager (MDI) and SOHO/Extreme Ultraviolet Imaging Telescope (EIT) instruments during 12 May?–?23 June 2003 is analysed. The overall detection performance of each algorithm is benchmarked against National Oceanic and Atmospheric Administration (NOAA) and Solar Influences Data Analysis Center (SIDC) catalogues using various feature properties such as total sunspot area, which shows good agreement, and the number of features detected, which shows poor agreement. Principal Component Analysis indicates a clear distinction between photospheric properties, which are highly correlated to the first component and account for 52.86% of variability in the data set, and coronal properties, which are moderately correlated to both the first and second principal components. Finally, case studies of NOAA 10377 and 10365 are conducted to determine algorithm stability for tracking the evolution of individual features. We find that magnetic flux and total sunspot area are the best indicators of active-region emergence. Additionally, for NOAA 10365, it is shown that the onset of flaring occurs during both periods of magnetic-flux emergence and complexity development.     

The Effect of Relative Sunspot Numbers,Solar Flare Numbers and Variable Component of 10.7 cm Solar Flux on The Seasonal Variation of 6300 Å Line Intensity at Calcutta

The paper presents the seasonal variation of 6300 Å line intensity at Calcutta with relative sunspot number, solar flare number and variable component of 10.7 cm solar flux. A study has been made and important results have been obtained which are as follows. (i) Intensity of 6300 Å line shows periodic variation with relative sunspot number, solar flare number and variable component of 10.7 cm solar flux during the period 1984–1986 which is the secondary peak of the descending phase of 21st solar cycle. (ii) 6300 Å line intensity at Cachoeira Paulista station, taken by Sahai et al. (1988), also shows periodic variation with solar parameters during the period 1978–1980 which is the peak phase of the solar cycle. (iii) A possible explanation of such a type of variation is also presented.     

Automatic Detection and Tracking of Coronal Mass Ejections in Coronagraph Time Series

We present the current capabilities of a software tool to automatically detect coronal mass ejections (CMEs) based on time series of coronagraph images: the solar eruptive event detection system (SEEDS). The software developed consists of several modules: preprocessing, detection, tracking, and event cataloging. The detection algorithm is based on a 2D to 1D projection method, where CMEs are assumed to be bright regions moving radially outward as observed in a running-difference time series. The height, velocity, and acceleration of the CME are automatically determined. A threshold-segmentation technique is applied to the individual detections to automatically extract an approximate shape of the CME leading edge. We have applied this method to a 12-month period of continuous coronagraph images sequence taken at a 20-minute cadence by the Large Angle and Spectrometric Coronagraph (LASCO) instrument (using the C2 instrument only) onboard the Solar and Heliospheric Observatory (SOHO) spacecraft. Our automated method, with a high computational efficiency, successfully detected about 75% of the CMEs listed in the CDAW CME catalog, which was created by using human visual inspection. Furthermore, the tool picked up about 100% more small-size or anomalous transient coronagraph events that were ignored by human visual inspection. The output of the software is made available online at . The parameters of scientific importance extracted by the software package are the position angle, angular width, velocity, peak, and average brightness. Other parameters could easily be added if needed. The identification of CMEs is known to be somewhat subjective. As our system is further developed, we expect to make the process significantly more objective.     

Seasonal Variation of 5577 å Line Emission At Calcutta and its Variation with Different Solar Parameters

The paper presents the variation of 5577 Å line intensity with relative sunspot number, and 10.7 cm solar flux. The study has obtained the following important results.[(i)] The 5577 Å line intensity at Calcutta is plotted against relative sunspot number, and the variable component of 10.7 cm solar flux during 1984–1985, which is the secondary peak of the descending phase of the 21st solar cycle. The intensity curves show periodic variation with different solar parameters.[(ii)] The 5577 Å line intensity at Mt. Abu also shows periodic variation with solar parameters during the period 1965–1968 when there was a peak phase of the 20th solar cycle.[(iii)] A possible explanation for such variation is also presented.     

方位指向检测系统的参数测定

针对低纬子午环的观测方式和方位定向系统的精度，叙述了配备方位指向检测系统的必要性，介绍了方位指向检测的原理和系统参数的测定方法，分析了在仪器的三个支承螺杆有晃动的情况下，系统参数测定的不可靠性，提出了克服参数测定不可靠性影响的方法。     

Hemispheric asymmetry of sunspot area in solar cycle 23 and rising phase of solar cycle 24: Comparison of three data sets

Analysis of long-term solar data from different observatories is required to compare and confirm the various level of solar activity in depth. In this paper, we study the north–south asymmetry of monthly mean sunspot area distribution during the cycle-23 and rising phase of cycle-24 using the data from Kodaikanal Observatory (KO), Michelson Doppler Imager (MDI) and Solar Optical Observing Network (SOON). Our analysis confirmed the double peak behavior of solar cycle-23 and the dominance of southern hemisphere in all the sunspot area data obtained from three different resources. The analysis also showed that there is a 5–6 months time delay in the activity levels of two hemispheres. Furthermore, the wavelet analysis carried on the same data sets showed several known periodicities (e.g., 170–180 days, 2.1 year) in the north–south difference of sunspot area data. The temporal occurrence of these periods is also the same in all the three data sets. These results could help in understanding the underlying mechanism of north–south asymmetry of solar activity.     

基于SIFT特征检测和密度峰值聚类的太阳活动区自动检测算法研究

太阳活动区是太阳大气中产生各种活动现象的区域,精确地检测和识别太阳活动区对理解太阳磁场的形成机制具有极为重要的科学意义.根据太阳活动区结构较为复杂的特点,基于尺度不变特征变换(ScaleInvariant Feature Transform, SIFT)和密度峰值聚类(Clustering by Fast Search and Find of Density Peaks,DPC)算法的优越性,提出了一种太阳活动区的自动检测和识别方法.首先,对太阳动力学天文台(Solar Dynamics Observatory, SDO)日震和磁场成像仪(Helioseismic and Magnetic Imager, HMI)的纵向磁图进行对比度增强;然后采用SIFT方法提取出全日面磁图中的特征点;最后利用DPC算法将特征点进行聚类,从而自动检测和识别出太阳活动区.研究结果表明, SIFT和DPC算法相结合的方法可以在不需要人工交互的情况下准确地自动检测出太阳活动区.     

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.     

An Automatic Detection for Solar Active Regions Based on Scale-Invariant Feature Transform and Clustering by Fast Search and Find of Density Peaks

The solar active regions are the regions where various active phenomena occur in the solar atmosphere. Accurate detection and identification of the solar active regions are of great scientific significance to understand the formation mechanism of the solar magnetic field. In this paper, we propose an automatic detection and recognition technology for solar active regions based on the advantages of Scale Invariant Feature Transform (SIFT) and Clustering by Fast Search and Find of Density Peaks (DPC). Firstly, enhance the contrast of longitudinal magnetic image of Helioseismic and Magnetic Imager (HMI) of Solar Dynamics Observatory (SDO). Then extract the feature points by SIFT. Finally, cluster the feature points by fast search and find of density peaks so as to automatically detect and identify the solar active regions. The results show that the combination of SIFT and DPC can accurately detect the solar active region without human-computer interaction.     

Distribution of CMEs with Different Angular Widths and Comparison with the Phase of Sunspot Number in the 23rd Solar Cycle

The variation of the number of coronal mass ejections (CMEs) with different angular widths in the period of 1996-2008 is analyzed statistically in this paper, together with a comparison of the feature of time variation between the number of CMEs with some typical angular widths and the number of sunspots.     

低纬子午环方位定向误差及其检测

本文分析了低纬子午环方位定向的误差来源和设置方位检测系统的必要性 ,给出了检测系统的参数 ,用实测值计算了钢球定位盘的精度和用检测值作修正的精度 ,这种修正精度是仅依靠提高仪器稳定性和加工精度所不能达到的 ,文中还叙述了检测系统存在的问题和改进方法。     

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.