Explore the vast range of titles available.

The term “ultraviolet (UV) burst” is introduced to describe small, intense, transient brightenings in ultraviolet images of solar active regions. We inventorize their properties and provide a definition based on image sequences in transition-region lines. Coronal signatures are rare, and most bursts are associated with small-scale, canceling opposite-polarity fields in the photosphere that occur in emerging flux regions, moving magnetic features in sunspot moats, and sunspot light bridges. We also compare UV bursts with similar transition-region phenomena found previously in solar ultraviolet spectrometry and with similar phenomena at optical wavelengths, in particular Ellerman bombs. Akin to the latter, UV bursts are probably small-scale magnetic reconnection events occurring in the low atmosphere, at photospheric and/or chromospheric heights. Their intense emission in lines with optically thin formation gives unique diagnostic opportunities for studying the physics of magnetic reconnection in the low solar atmosphere. This paper is a review report from an International Space Science Institute team that met in 2016–2017.

We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), taken over a five-year interval (2012 – 2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several machine learning (ML) and conventional statistics techniques to predict flares of peak magnitude > M1 and > C1 within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM), and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second-best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best-performing method gives accuracy ACC = 0.93 ( 0.00 ) , true skill statistic TSS = 0.74 ( 0.02 ) , and Heidke skill score HSS = 0.49 ( 0.01 ) for > M1 flare prediction with probability threshold 15% and ACC = 0.84 ( 0.00 ) , TSS = 0.60 ( 0.01 ) , and HSS = 0.59 ( 0.01 ) for > C1 flare prediction with probability threshold 35%.

It has been a prevailing picture that active regions on the solar surface originate from a strong toroidal magnetic field stored in the overshoot region at the base of the solar convection zone, generated by a deep seated solar dynamo mechanism. This article reviews the studies in regard to how the toroidal magnetic field can destabilize and rise through the convection zone to form the observed solar active regions at the surface. Furthermore, new results from the global simulations of the convective dynamos, and from the near-surface layer simulations of active region formation, together with helioseismic investigations of the pre-emergence active regions, are calling into question the picture of active regions as buoyantly rising flux tubes originating from the bottom of the convection zone. This article also gives a review on these new developments.

The precise physical process that triggers solar flares is not currently understood. Here we attempt to capture the signature of this mechanism in solar-image data of various wavelengths and use these signatures to predict flaring activity. We do this by developing an algorithm that i) automatically generates features in 5.5 TB of image data taken by the Solar Dynamics Observatory of the solar photosphere, chromosphere, transition region, and corona during the time period between May 2010 and May 2014, ii) combines these features with other features based on flaring history and a physical understanding of putative flaring processes, and iii) classifies these features to predict whether a solar active region will flare within a time period of T hours, where T = 2  and  24 . Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We find that when optimizing for the True Skill Score (TSS), photospheric vector-magnetic-field data combined with flaring history yields the best performance, and when optimizing for the area under the precision–recall curve, all of the data are helpful. Our model performance yields a TSS of 0.84 ± 0.03 and 0.81 ± 0.03 in the T = 2 - and 24-hour cases, respectively, and a value of 0.13 ± 0.07 and 0.43 ± 0.08 for the area under the precision–recall curve in the T = 2 - and 24-hour cases, respectively. These relatively high scores are competitive with previous attempts at solar prediction, but our different methodology and extreme care in task design and experimental setup provide an independent confirmation of these results. Given the similar values of algorithm performance across various types of models reported in the literature, we conclude that we can expect a certain baseline predictive capacity using these data. We believe that this is the first attempt to predict solar flares using photospheric vector-magnetic field data as well as multiple wavelengths of image data from the chromosphere, transition region, and corona, and it points the way towards greater data integration across diverse sources in future work.

The emergence of active regions on the Sun is an integral feature of the solar dynamo mechanism. However, details about the generation of active-region-scale magnetism and the journey of this magnetic flux from the interior to the photosphere are still in question. Shifting paradigms are now developing for the source depth of the Sun’s large-scale magnetism, the organization of this magnetism into fibril flux tubes, and the role of convection in shaping active-region observables. Here we review the landscape of flux emergence theories and simulations, highlight the role flux emergence plays in the global dynamo process, and make connections between flux emergence on the Sun and other cool stars. As longer-term and higher fidelity observations of both solar active regions and their associated flows are amassed, it is now possible to place new constraints on models of emerging flux. We discuss the outcomes of statistical studies which provide observational evidence that flux emergence may be a more passive process (at least in the upper convection zone); dominated to a greater extent by the influence of convection and to a lesser extent by buoyancy and the Coriolis force acting on rising magnetic flux tubes than previously thought. We also discuss how the relationship between stellar rotation, fractional convection zone depth, and magnetic activity on other stars can help us better understand the flux emergence processes. Looking forward, we identify open questions regarding magnetic flux emergence that we anticipate can be addressed in the next decade with further observations and simulations.

Solar activity is often caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions (ARs) have been used to analyze and forecast eruptive events, such as solar flares and coronal mass ejections. Unfortunately, the most recent Solar Cycle 24 was relatively weak with few large flares, though it is the only solar cycle in which consistent time-sequence vector magnetograms have been available through the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) since its launch in 2010. In this work, we look into another major instrument, namely the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO) from 1996 to 2010. The data archive of SOHO/MDI covers a more active Solar Cycle 23 with many large flares. However, SOHO/MDI only has line-of-sight (LOS) magnetograms. We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, B x and B y , taken by SDO/HMI, along with H α observations collected by the Big Bear Solar Observatory (BBSO), and to generate synthetic vector components B x ′ and B y ′ of ARs. These generated vector components, together with observational LOS data, would form vector magnetograms for SOHO/MDI. In this way, we can expand the availability of vector magnetograms to the period from 1996 to present. Experimental results demonstrate the good performance of the MagNet method. To our knowledge, this is the first time that deep learning has been used to generate photospheric vector magnetograms of ARs for SOHO/MDI using SDO/HMI and H α data.

Image super-resolution is an important subject in image processing and recognition. Here, we present an attention-aided convolutional neural network for solar image super-resolution. Our method, named SolarCNN, aims to enhance the quality of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO). The ground-truth labels used for training SolarCNN are the LOS magnetograms collected by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Solar ARs consist of strong magnetic fields in which magnetic energy can suddenly be released to produce extreme space-weather events, such as solar flares, coronal mass ejections, and solar energetic particles. SOHO/MDI covers Solar Cycle 23, which is stronger with more eruptive events than Cycle 24. Enhanced SOHO/MDI magnetograms allow for better understanding and forecasting of violent events of space weather. Experimental results show that SolarCNN improves the quality of SOHO/MDI magnetograms in terms of the structural similarity index measure, Pearson’s correlation coefficient, and the peak signal-to-noise ratio.

We study a complex GOES M1.1 circular ribbon flare and related pre-flare activity on 26 January 2015 [SOL2015-01-26T16:53] in the solar active region NOAA 12268. This flare activity was observed by the Atmospheric Imaging Assembly (AIA) on board Solar Dynamics Observatory (SDO) and the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). The examination of photospheric magnetograms during the extended period, prior to the event, suggests the successive development of a so-called “anemone” type magnetic configuration. The Nonlinear Force Free Field (NLFFF) extrapolation reveals a fan-spine magnetic configuration with the presence of a coronal null-point. We found that the pre-flare activity in the active region starts ≈15 min prior to the main flare in the form of localized bright patches at two locations. A comparison of locations and spatial structures of the pre-flare activity with magnetic configuration of the corresponding region suggests onset of magnetic reconnection at the null-point along with the low-atmosphere magnetic reconnection caused by the emergence and the cancellation of the magnetic flux. The main flare of M1.1 class is characterized by the formation of a well-developed circular ribbon along with a region of remote brightening. Remarkably, a set of relatively compact parallel ribbons formed inside the periphery of the circular ribbon which developed lateral to the brightest part of the circular ribbon. During the peak phase of the flare, a coronal jet is observed at the north-east edge of the circular ribbon, which suggests interchange reconnection between large-scale field lines and low-lying closed field lines. Our investigation suggests a combination of two distinct processes in which ongoing pre-flare null-point reconnection gets further intensified as the confined eruption along with jet activity proceeded from within the circular ribbon region which results to the formation of inner parallel ribbons and corresponding post-reconnection arcade.

We used magnetograms acquired with the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) to calculate and analyze spatial correlation functions and the multifractal spectra in solar active regions (ARs). The analysis was performed for two very different types of ARs: i) simple bipolar magnetic structures with regular orientation (the magnetomorphological class A1), and ii) very complex multipolar ARs (the magnetomorphological class B3). All ARs were explored at the developed phase during flareless periods. For correlation functions, the power-law and exponential approximations were calculated and compared. It was found that the exponential law holds for the correlation functions of both types of ARs within spatial scales of 1 – 36 Mm, while the power law failed to approximate the observed correlation functions. The property of multifractality was found in all ARs, being more pronounced for the complex B3-class ARs. Our results might imply that the photospheric magnetic field of an AR is a self-organized system, which, however, does not exhibit properties of self-organized criticality (SOC), and its fractal properties are an attribute of a broader (than SOC only) class of nonlinear systems.

Sunquakes are seismic emissions visible on the solar surface, associated with some solar flares. Although discovered in 1998, they have only recently become a more commonly detected phenomenon. Despite the availability of several manual detection guidelines, to our knowledge, the astrophysical data produced for sunquakes is new to the field of machine learning. Detecting sunquakes is a daunting task for human operators, and this work aims to ease and, if possible, to improve their detection. Thus, we introduce a dataset constructed from acoustic egression-power maps of solar active regions obtained for Solar Cycles 23 and 24 using the holography method. We then present a pedagogical approach to the application of machine-learning representation methods for sunquake detection using autoencoders, contrastive learning, object detection and recurrent techniques, which we enhance by introducing several custom, domain-specific data augmentation transformations. We address the main challenges of the automated sunquake-detection task, namely the very high noise patterns in and outside the active region shadow and the extreme class imbalance given by the limited number of frames that present sunquake signatures. With our trained models, we find temporal and spatial locations of peculiar acoustic emission and qualitatively associate them to eruptive and high energy emission. While noting that these models are still in a prototype stage, and there is much room for improvement in metrics and bias levels, we hypothesize that their agreement on example use cases has the potential to enable detection of weak solar acoustic manifestations.