Probing the Fundamental Physics of the Solar Corona with Lunar Solar Occultation Observations

Imaging and spectroscopy of the solar corona, coupled with polarimetry, are the only tools available at present to capture signatures of physical processes responsible for coronal heating and solar wind acceleration within the first few solar radii above the solar limb. With the recent advent of improved detector technology and image processing techniques, broad-band white light and narrow-band multi-wavelength observations of coronal forbidden lines, made during total solar eclipses, have started to yield new views about the thermodynamic and magnetic properties of coronal structures. This paper outlines these unique capabilities, which until present, have been feasible primarily with observations during natural total solar eclipses. This work also draws attention to the exciting possibility of greatly increasing the frequency and duration of solar eclipse observations with Moon orbiting observatories utilizing lunar limb occultation of the solar disk for coronal measurements.     

Probing the Quiet Solar Atmosphere from the Photosphere to the Corona

We investigate the morphology and temporal variability of a quiet-Sun network region in different solar layers. The emission in several extreme ultraviolet (EUV) spectral lines through both raster and slot time-series, recorded by the EUV Imaging Spectrometer (EIS) on board the Hinode spacecraft is studied along with \(\mbox{H}\upalpha\) observations and high-resolution spectropolarimetric observations of the photospheric magnetic field. The photospheric magnetic field is extrapolated up to the corona, showing a multitude of large- and small-scale structures. We show for the first time that the smallest magnetic structures at both the network and internetwork contribute significantly to the emission in EUV lines, with temperatures ranging from \(8\times 10^{4}~\mbox{K}\) to \(6\times 10^{5}~\mbox{K}\). Two components of transition region emission are present, one associated with small-scale loops that do not reach coronal temperatures, and another component that acts as an interface between coronal and chromospheric plasma. Both components are associated with persistent chromospheric structures. The temporal variability of the EUV intensity at the network region is also associated with chromospheric motions, pointing to a connection between transition region and chromospheric features. Intensity enhancements in the EUV transition region lines are preferentially produced by \(\mbox{H}\upalpha\) upflows. Examination of two individual chromospheric jets shows that their evolution is associated with intensity variations in transition region and coronal temperatures.     

Nowcasting Solar Energetic Particle Events Using Principal Component Analysis

We perform a principal component analysis (PCA) on a set of six solar variables (i.e. width/size (\(s\)) and velocity (\(u\)) of a coronal mass ejection, logarithm of the solar flare (SF) magnitude (\(\log\mathit{SXRs}\)), SF longitude (\(\mathit{lon}\)), duration (\(\mathit{DT}\)), and rise time (\(\mathit{RT}\))). We classify the solar energetic particle (SEP) event radiation impact (in terms of the National Oceanic and Atmospheric Administration scales) with respect to the characteristics of their parent solar events. We further attempt to infer the possible prediction of SEP events. In our analysis, we use 126 SEP events with complete solar information, from 1997 to 2013. Each SEP event is a vector in six dimensions (corresponding to the six solar variables used in this work). The PCA transforms the input vectors into a set of orthogonal components. By mapping the characteristics of the parent solar events, a new base defined by these components led to the classification of the SEP events. We furthermore applied logistic regression analysis with single, as well as multiple explanatory variables, in order to develop a new index (\(I\)) for the nowcasting (short-term forecasting) of SEP events. We tested several different schemes for \(I\) and validated our findings with the implementation of categorical scores (probability of detection (POD) and false-alarm rate (FAR)). We present and interpret the obtained scores, and discuss the strengths and weaknesses of the different implementations. We show that \(I\) holds prognosis potential for SEP events. The maximum POD achieved is 77.78% and the relative FAR is 40.96%.     

Probing Twisted Magnetic Field Using Microwave Observations in an M Class Solar Flare on 11 February, 2014

This work demonstrates the possibility of magnetic-field topology investigations using microwave polarimetric observations. We study a solar flare of GOES M1.7 class that occurred on 11 February, 2014. This flare revealed a clear signature of spatial inversion of the radio-emission polarization sign. We show that the observed polarization pattern can be explained by nonthermal gyrosynchrotron emission from the twisted magnetic structure. Using observations of the Reuven Ramaty High Energy Solar Spectroscopic Imager, Nobeyama Radio Observatory, Radio Solar Telescope Network, and Solar Dynamics Observatory, we have determined the parameters of nonthermal electrons and thermal plasma and identified the magnetic structure where the flare energy release occurred. To reconstruct the coronal magnetic field, we use nonlinear force-free field (NLFFF) and potential magnetic-field approaches. Radio emission of nonthermal electrons is simulated by the GX Simulator code using the extrapolated magnetic field and the parameters of nonthermal electrons and thermal plasma inferred from the observations; the model radio maps and spectra are compared with observations. We have found that the potential-magnetic-field approach fails to explain the observed circular polarization pattern; on the other hand, the Stokes-\(V\) map is successfully explained by assuming nonthermal electrons to be distributed along the twisted magnetic structure determined by the NLFFF extrapolation approach. Thus, we show that the radio-polarization maps can be used for diagnosing the topology of the flare magnetic structures where nonthermal electrons are injected.     

Identification of Sunspots on Full-Disk Solar Images Using Wavelet Analysis

A new method to detect sunspots on full-disk solar images, recorded in an optical wide spectral band and in the line of Ca ii K₁, is presented. It is based on the compact wavelet transform and a process that automatically identifies the noise in the images of the Sun. This method estimates also its center and the limb positions with a precision of a few milliarcseconds. Precisions of 1 and 8 milliarcseconds are, respectively, found for the disk center position and the radius of simulated solar images. The results obtained on sunspot identification on solar images recorded during April 2002 at Meudon Observatory (France) reveal that we are able to detect 3.35 % additional sunspots compared to a manual method. Our method preserves the sunspot areas better than 95 %.     

Prediction of Solar Cycle Maximum Using Solar Cycle Lengths

If the rise time RT, fall time FT, and total time TT (i.e., RT+FT) of a solar cycle are compared against the maximum amplitude Rz(max ) for the following cycle, then only the association between TT and Rz(max ) is inferred to be well anticorrelated, inferring that the larger (smaller) the value of Rz(max ) for the following cycle, the shorter (longer) the TT of the preceding cycle. Although the inferred correlation (−0.68) is statistically significant, the inferred standard error of estimate is quite large, so predictions using the inferred correlation are not very precise. Removal of cycle pairs 15/16, 19/20, and 20/21 (statistical outliers) yields a regression that is highly statistically significant (−0.85) and reduces the standard error of estimate by 18%. On the basis of the adjusted regression and presuming TT=140 months for cycle 23, the present ongoing cycle, cycle 24’s 90% prediction interval for Rz(max ) is estimated to be about 94±44, inferring only a 5% probability that its Rz(max ) will be larger than about 140, unless of course cycle pair 23/24 is a statistical outlier.     

Decoupling Solar Variability and Instrument Trends Using the Multiple Same-Irradiance-Level (MuSIL) Analysis Technique

The solar spectral irradiance (SSI) dataset is a key record for studying and understanding the energetics and radiation balance in Earth’s environment. Understanding the long-term variations of the SSI over timescales of the 11-year solar activity cycle and longer is critical for many Sun–Earth research topics. Satellite measurements of the SSI have been made since the 1970s, most of them in the ultraviolet, but recently also in the visible and near-infrared. A limiting factor for the accuracy of previous solar variability results is the uncertainties for the instrument degradation corrections, which need fairly large corrections relative to the amount of solar cycle variability at some wavelengths. The primary objective of this investigation has been to separate out solar cycle variability and any residual uncorrected instrumental trends in the SSI measurements from the Solar Radiation and Climate Experiment (SORCE) mission and the Thermosphere, Mesosphere, Ionosphere, Energetic, and Dynamics (TIMED) mission. A new technique called the Multiple Same-Irradiance-Level (MuSIL) analysis has been developed, which examines an SSI time series at different levels of solar activity to provide long-term trends in an SSI record, and the most common result is a downward trend that most likely stems from uncorrected instrument degradation. This technique has been applied to each wavelength in the SSI records from SORCE (2003?–?present) and TIMED (2002?–?present) to provide new solar cycle variability results between 27 nm and 1600 nm with a resolution of about 1 nm at most wavelengths. This technique, which was validated with the highly accurate total solar irradiance (TSI) record, has an estimated relative uncertainty of about 5% of the measured solar cycle variability. The MuSIL results are further validated with the comparison of the new solar cycle variability results from different solar cycles.     

Longterm Prediction of Solar Activity Using the Combined Method

The Combined Method is a non-parametric regression technique for long-term prediction of smoothed monthly sunspot numbers. Starting from a solar minimum, a prediction of the succeeding maximum is obtained by using a dynamo-based relation between the geomagnetic aa index and succeeding solar maxima. Then a series of predictions is calculated by computing the weighted average of past cycles of similar level. This technique leads to a good prediction performance, particularly in the ascending phase of the solar cycle where purely statistical methods tend to be inaccurate. For cycle 23 the combined method predicts a maximum of 160 (in terms of smoothed sunspot number) early in the year 2000.     

Long-Term Modulation of Cosmic-Ray Intensity and Correlation Analysis Using Solar and Heliospheric Parameters

Based on the monthly sunspot numbers (SSNs), the solar-flare index (SFI), grouped solar flares (GSFs), the tilt angle of heliospheric current sheet (HCS), and cosmic-ray intensity (CRI) for Solar Cycles 21?–?24, a detailed correlation study has been performed using the cycle-wise average correlation (with and without time lag) method as well as by the “running cross-correlation” method. It is found that the slope of regression lines between SSN and SFI, as well as between SSN and GSF, is continuously decreasing from Solar Cycle 21 to 24. The length of regression lines has significantly decreased during Cycles 23 and 24 in comparison to Cycles 21 and 22. The cross-correlation coefficient (without time lag) between SSN–CRI, SFI–CRI, and GSF–CRI has been found to be almost the same during Cycles 21 and 22, while during Cycles 23 and 24 it is significantly higher between SSN–CRI and HCS–CRI than for SFI–CRI and GSF–CRI. Considering time lags of 1 to 20 months, the maximum correlation coefficient (negative) amongst all of the sets of solar parameters is observed with almost the same time lags during Cycles 21?–?23, whereas exceptional behaviour of the time lag has been observed during Cycle 24, as the correlation coefficient attains its maximum value with two time lags (four and ten months) in the case of the SSN–CRI relationship. A remarkably large time lag (22 months) between HCS and CRI has been observed during the odd-numbered Cycle 21, whereas during another odd cycle, Cycle 23, the lag is small (nine months) in comparison to that for other solar/flare parameters (13?–?15 months). On the other hand, the time lag between SSN–CRI and HCS–CRI has been found to be almost the same during even-numbered Solar Cycles 22 and 24. A similar analysis has been performed between SFI and CRI, and it is found that the correlation coefficient is maximum at zero time lag during the present solar cycle. The GSFs have shown better maximum correlation with CRI as compared to SFI during Cycles 21 to 23, indicating that GSF could also be used as a significant solar parameter to study the cosmic-ray modulation. Furthermore, the running cross-correlation coefficient between SSN–CRI and HCS–CRI, as well as between solar-flare activity parameters (SFI and GSF) and CRI is observed to be strong during the ascending and descending phases of solar cycles. The level of cosmic-ray modulation during the period of investigation shows the appropriateness of different parameters in different cycles, and even during the different phases of a particular solar cycle. We have also studied the galactic cosmic-ray modulation in relation to combined solar and heliospheric parameters using the empirical model suggested by Paouris et al. (Solar Phys.280, 255, 2012). The proposed model for the calculation of the modulated cosmic-ray intensity obtained from the combination of solar and heliospheric parameter gives a very satisfactory value of standard deviation as well as \(R^{2}\) (the coefficient of determination) for Solar Cycles 21?–?24.     

The Search for Solar Gravity-Mode Oscillations in the Solar Wind Using it Ulysses Plasma Data

A recent report that energetic particles measured in the solar wind may be influenced by solar gravity-mode ( -mode) oscillations motivated the search for -mode signatures in the Ulysses solar wind plasma data. Ulysses solar wind plasma data from 1 March 1992 through the 12 April 1996 were examined in this study for signs of possible solar oscillations. The multi-taper method for spectral analysis was used to look for significant spectral peaks in the entire four-year data set, as well as in the smaller, more heliographically homogenous data set over the solar poles. Several frequencies satisfying certain significance requirements were found in the -mode frequency range in both data sets that also agree with the previously published findings. However, these identifications are shown to be false detections, and hence the frequencies found cannot be identified as solar modes.     

太阳活动与太阳自转速率变化关系的分析

研究发现,太阳自转速率的变化与太阳活动之间存在一定的联系,但是不同学者的研究结论存在着矛盾:有的认为两者为正相关,而有的却认为是负相关.究竟两者之间是什么关系,需要做进一步深入的分析.利用EEMD (Ensemble Empirical Mode Decomposition)等方法对太阳自转速率和太阳黑子数据序列进行相关关系以及相位关系的计算和分析,以探讨太阳自转速率变化与太阳活动之间的关系.研究发现:两者的长期趋势项分量呈显著负相关;在11 yr左右周期分量上,观测到的太阳自转速率滞后太阳黑子的变化约2 yr时,呈显著负相关关系,超前3 yr时呈现次显著的正相关;对太阳活动第12–23周各周内部太阳黑子与太阳自转速率的相关分析表明,两者的关系比较复杂,但负相关关系更为显著.这为进一步理解太阳活动变化与太阳自转速率变化之间的成因联系提供了新的依据.     

Studies of Solar Helicity Using Vector Magnetograms

Observations of photospheric magnetic fields made with vector magnetographs have been used recently to study helicity on the Sun. In this paper we indicate what can and cannot be derived from vector magnetograms, and point out some potential problems in these data that could affect the calculations of `helicity'. Among these problems are magnetic saturation, Faraday rotation, low spectral resolution, and the method of resolving the ambiguity in the azimuth.     

Automatic Solar Flare Tracking Using Image-Processing Techniques

Measurement of the evolution properties of solar flares through their complete cyclic development is crucial in the studies of Solar Physics. From the analysis of solar H images, we used Support Vector Machines (SVMs) to automatically detect flares and applied image segmentation techniques to compute their properties. We also present a solution for automatically tracking the apparent separation motion of two-ribbon flares and measuring their moving direction and speed in the magnetic fields. From these measurements, with certain assumptions, we inferred the reconnection of the electric field as a measure of the rate of the magnetic reconnection in the corona. The automatic procedure is a valuable tool for real-time monitoring of flare evolution.     

Short-Term Solar Flare Prediction Using Predictor Teams

A short-term solar flare prediction model is built using predictor teams rather than an individual set of predictors. The information provided by the set of predictors could be redundant. So it is necessary to generate subsets of predictors which can keep the information constant. These subsets are called predictor teams. In the framework of rough set theory, predictor teams are constructed from sequences of the maximum horizontal gradient, the length of neutral line and the number of singular points extracted from SOHO/MDI longitudinal magnetograms. Because of the instability of the decision tree algorithm, prediction models generated by the C4.5 decision tree for different predictor teams are diverse. The flaring sample, which is incorrectly predicted by one model, can be correctly forecasted by another one. So these base prediction models are used to construct an ensemble prediction model of solar flares by the majority voting rule. The experimental results show that the predictor team can keep the distinguishability of the original set, and the ensemble prediction model can obtain better performance than the model based on the individual set of predictors.     

A Statistical Study on the Reconnection in Boundary Layers of Small-scale Magnetic Flux Tubes in Solar Winds

With the data from WIND satellite in 1995—2005, the small-scale magnetic ?ux tubes determined with two methods, i.e., the arti?cial recognition and program selection respectively, are compared. It is found that there are magnetic reconnections in the boundary layers of 41% small-scale magnetic ?ux tubes determined with the program selection method, which is similar to the re- sult of small-scale magnetic ?ux tubes determined with the method of arti?cial recognition. The features of magnetic reconnections, such as the magnetic shear angle, magnetic ?eld strength, and duration of the dissipation region of recon- nection at the small-scale ?ux tubes’ boundaries determined from both methods have the same statistical tendency. This shows that there is no essential differ- ence in the properties of reconnections in the boundary layers of the small-scale magnetic ?ux tubes determined with the two methods. Hence the data yielded by both methods can be used as the samples for statistically studying the events of reconnection in the front and back boundary layers of small-scale ?ux ropes. There are totally 71 magnetic reconnection events selected in this paper. Our statistical result shows that in 50 events (70%) the decrease of magnetic ?eld strength in the dissipation region of reconnection is larger than 20%, and in 47 events (66%) the magnetic shear angle is larger than 90 degrees. These indicate that the magnetic reconnections in boundaries of the small-scale magnetic ?ux tubes are more likely to be anti-parallel. The statistics has been performed sep- arately in the reconnections of the front and back boundary layers of small-scale magnetic ?ux tubes. The results show that the features of reconnections in the front and back boundaries are similar to each other, which is different from the various properties in the front and back boundaries of the magnetic clouds, and this means that the expansion in the large-scale magnetic ?ux tubes, such as magnetic clouds, does not happen in the small-scale magnetic ?ux tubes.     

Automated Detection of Low-Contrast Solar Features Using the Phase-Congruency Algorithm

We propose a new feature-detection technique based on phase-congruency (PC) measurements to automatically recognize or enhance faint features in solar observations, such as off-limb coronal loops and umbral dots. Compared with other feature-detection methods that are based on gradient illuminance and imaging filtering, PC-based measurements are particular efficient for recognizing faint features, which generally have a low-intensity contrast to their surroundings. In the present article, we carry out a PC-based measurement of the local weighted mean phase angle (LWMPA) at each point in an image to indicate or highlight low-contrast features. We first used artificial images to check the detection accuracy and sensitivity to the noise of this approach. Subsequently, we applied this approach to an EUV observation obtained by the Solar Dynamics Observatory/Atmospheric Imaging Assembly to highlight off-limb coronal loops, and a photospheric observation obtained by the Hinode/Solar Optical Telescope to recognize faint dots within the cores of sunspots and pores. The results illustrate that this PC-based measurement of the LWMPA is a robust detection method for faint structures in solar observations.     

Visibility-Graph Analysis of the Solar Wind Velocity

We analyze in situ measurements of the solar wind velocity obtained by the Advanced Composition Explorer (ACE) and the Helios spacecraft during the years 1998?–?2012 and 1975?–?1983, respectively. The data mainly belong to solar cycles 23 (1996?–?2008) and 21 (1976?–?1986). We used the directed horizontal-visibility-graph (DHVg) algorithm and estimated a graph functional, namely, the degree distance (D), which is defined using the Kullback–Leibler divergence (KLD) to understand the time irreversibility of solar wind time-series. We estimated this degree-distance irreversibility parameter for these time-series at different phases of the solar activity cycle. The irreversibility parameter was first established for known dynamical data and was then applied to solar wind velocity time-series. It is observed that irreversibility in solar wind velocity fluctuations show a similar behavior at 0.3 AU (Helios data) and 1 AU (ACE data). Moreover, the fluctuations change over the phases of the activity cycle.     

Automatic Solar Flare Detection Using Neural Network Techniques

We present a new method for automatic detection of flare events from images in the optical range. The method uses neural networks for pattern recognition and is conceived to be applied to full-disk Himages. Images are analyzed in real time, which allows for the design of automatic patrol processes able to detect and record flare events with the best time resolution available without human assistance. We use a neural network consisting of two layers, a hidden layer of nonlinear neurodes and an output layer of one linear neurode. The network was trained using a back-propagation algorithm and a set of full-disk solar images obtained by HASTA (HSolar Telescope for Argentina), which is located at the Estación de Altura Ulrico Cesco of OAFA (Observatorio Astronómico Félix Aguilar), El Leoncito, San Juan, Argentina. This method is appropriate for the detection of solar flares in the complete optical classification, being portable to any Hinstrument and providing unique criteria for flare detection independent of the observer.