Explore the vast range of titles available.

The detection and characterization of self-organized criticality (SOC), in both real and simulated data, has undergone many significant revisions over the past 25 years. The explosive advances in the many numerical methods available for detecting, discriminating, and ultimately testing, SOC have played a critical role in developing our understanding of how systems experience and exhibit SOC. In this article, methods of detecting SOC are reviewed; from correlations to complexity to critical quantities. A description of the basic autocorrelation method leads into a detailed analysis of application-oriented methods developed in the last 25 years. In the second half of this manuscript space-based, time-based and spatial-temporal methods are reviewed and the prevalence of power laws in nature is described, with an emphasis on event detection and characterization. The search for numerical methods to clearly and unambiguously detect SOC in data often leads us outside the comfort zone of our own disciplines—the answers to these questions are often obtained by studying the advances made in other fields of study. In addition, numerical detection methods often provide the optimum link between simulations and experiments in scientific research. We seek to explore this boundary where the rubber meets the road, to review this expanding field of research of numerical detection of SOC systems over the past 25 years, and to iterate forwards so as to provide some foresight and guidance into developing breakthroughs in this subject over the next quarter of a century.

The current fleet of space-based solar observatories offers us a wealth of opportunities to study solar flares over a range of wavelengths. Significant advances in our understanding of flare physics often come from coordinated observations between multiple instruments. Consequently, considerable efforts have been, and continue to be, made to coordinate observations among instruments ( e.g. through the Max Millennium Program of Solar Flare Research ). However, there has been no study to date that quantifies how many flares have been observed by combinations of various instruments. Here we describe a technique that retrospectively searches archival databases for flares jointly observed by the Ramaty High Energy Solar Spectroscopic Imager (RHESSI), Solar Dynamics Observatory (SDO)/ EUV Variability Experiment (EVE – Multiple EUV Grating Spectrograph (MEGS)-A and -B, Hinode /( EUV Imaging Spectrometer , Solar Optical Telescope , and X-Ray Telescope ), and Interface Region Imaging Spectrograph (IRIS). Out of the 6953 flares of GOES magnitude C1 or greater that we consider over the 6.5 years after the launch of SDO, 40 have been observed by 6 or more instruments simultaneously. Using each instrument’s individual rate of success in observing flares, we show that the numbers of flares co-observed by 3 or more instruments are higher than the number expected under the assumption that the instruments operated independently of one another. In particular, the number of flares observed by larger numbers of instruments is much higher than expected. Our study illustrates that these missions often acted in cooperation, or at least had aligned goals. We also provide details on an interactive widget (Solar Flare Finder), now available in SSWIDL, which allows a user to search for flaring events that have been observed by a chosen set of instruments. This provides access to a broader range of events in order to answer specific science questions. The difficulty in scheduling coordinated observations for solar-flare research is discussed with respect to instruments projected to begin operations during Solar Cycle 25, such as the Daniel K. Inouye Solar Telescope , Solar Orbiter , and Parker Solar Probe .

Solar flares are events of intense scientific interest. Although certain solar conditions are known to be associated with flare activity, the exact location and timing of an individual flare on the Sun cannot as yet be predicted with certainty. Missions whose science objectives depend on observing solar flares must often make difficult decisions on where to target their observations if they do not observe the full solar disk. Yet, there is little analysis in the literature that might guide these mission operations to maximize their opportunities to observe flares. In this study, we analyze and simulate the performance of different observation strategies using historical flare and active region data from 2011 to 2014. We test a number of different target selection strategies based on active region complexity and recent flare activity, each of which is examined under a range of operational assumptions. In each case, we investigate various metrics such as the number of flares observed, the size of flares observed, and operational considerations such as the number of instrument repoints required. Overall, target selection methods based on recent flare activity showed the best overall performance but required more repointings than other methods. The mission responsiveness to new information is identified as another strong factor determining flare observation performance. It is also shown that target selection methods based on active region complexities show a significant pointing bias toward the western solar hemisphere. As expected, the number of flares observed grows quickly with field-of-view size until the approximate size of an active region is reached, but further improvements beyond the active region size are much more incremental. These results provide valuable performance estimates for a future mission focused on solar flares and inform the requirements that would ensure mission success.

Spectral-line fitting problems are extremely common in all remote-sensing disciplines, solar physics included. Spectra in solar physics are frequently parameterized by using a model for the background and the emission lines, and various computational techniques are used to find values to the parameters given the data. However, the most commonly-used techniques, such as least-squares fitting, are highly dependent on the initial parameter values used and are therefore biased. In addition, these routines occasionally fail because of ill-conditioning. Simulated annealing and Bayesian posterior distribution analysis offer different approaches to finding parameter values through a directed, but random, search of the parameter space. The algorithms proposed here easily incorporate any other available information about the emission spectrum, which is shown to improve the fit. Example algorithms are given and their performance is compared to a least-squares algorithm for test data - a single emission line, a blended line, and very low signal-to-noise-ratio data. It is found that the algorithms proposed here perform at least as well or better than standard fitting practices, particularly in the case of very low signal-to-noise ratio data. A hybrid simulated annealing and Bayesian posterior algorithm is used to analyze a Mg x line contaminated by an O IV triplet, as observed by the Coronal Diagnostic Spectrometer onboard SOHO. The benefits of these algorithms are also discussed.

Extreme ultraviolet (EUV) waves are large-scale propagating disturbances observed in the solar corona, frequently associated with coronal mass ejections and flares. They appear as faint, extended structures propagating from a source region across the structured solar corona. Since their discovery, over 200 papers discussing their properties, causes, and physical nature have been published. However, despite this their fundamental properties and the physics of their interactions with other solar phenomena are still not understood. To further the understanding of EUV waves, we have constructed the Automated Wave Analysis and Reduction (AWARE) algorithm for the measurement of EUV waves. AWARE is implemented in two stages. In the first stage, we use a new type of running difference image, the running difference persistence image, which enables the efficient isolation of propagating, bright wavefronts as they travel across the corona. In the second stage, AWARE detects the presence of a wavefront, and measures the distance, velocity, and acceleration of that wavefront across the Sun. The fit of propagation models to the wave progress isolated in the first stage is achieved using the Random Sample Consensus (RANSAC) algorithm. AWARE is tested against simulations of EUV wave propagation, and is applied to measure EUV waves in observational data from the Atmospheric Imaging Assembly (AIA). We also comment on unavoidable systematic errors that bias the estimation of wavefront velocity and acceleration. In addition, the full AWARE software suite comes with a package that creates simulations of waves propagating across the disk from arbitrary starting points.

The SunPy Project developed a 13-question survey to understand the software and hardware usage of the solar-physics community. Of the solar-physics community, 364 members across 35 countries responded to our survey. We found that 99 ± 0.5 % of respondents use software in their research and 66% use the Python scientific-software stack. Students are twice as likely as faculty, staff scientists, and researchers to use Python rather than Interactive Data Language (IDL). In this respect, the astrophysics and solar-physics communities differ widely: 78% of solar-physics faculty, staff scientists, and researchers in our sample uses IDL, compared with 44% of astrophysics faculty and scientists sampled by Momcheva and Tollerud ( 2015 ). 63 ± 4 % of respondents have not taken any computer-science courses at an undergraduate or graduate level. We also found that most respondents use consumer hardware to run software for solar-physics research. Although 82% of respondents work with data from space-based or ground-based missions, some of which ( e.g. the Solar Dynamics Observatory and Daniel K. Inouye Solar Telescope ) produce terabytes of data a day, 14% use a regional or national cluster, 5% use a commercial cloud provider, and 29% use exclusively a laptop or desktop. Finally, we found that 73 ± 4 % of respondents cite scientific software in their research, although only 42 ± 3 % do so routinely.

We present an automated approach to active region extraction from full-disc MDI longitudinal magnetograms. This uses a region-growing technique in conjunction with boundary-extraction to define a number of enclosed contours as belonging to separate regions of magnetic significance on the solar disc. This provides an objective definition of active regions and areas of plage on the Sun. A number of parameters relating to the flare potential of each region are discussed.

The current fleet of space-based solar observatories offers us a wealth of opportunities to study solar flares over a range of wavelengths. Significant advances in our understanding of flare physics often come from coordinated observations between multiple instruments. Consequently, considerable efforts have been, and continue to be made to coordinate observations among instruments (e.g. through the Max Millennium Program of Solar Flare Research). However, there has been no study to date that quantifies how many flares have been observed by combinations of various instruments. Here we describe a technique that retrospectively searches archival databases for flares jointly observed by RHESSI, SDO/EVE (MEGS-A and -B), Hinode/(EIS, SOT, and XRT), and IRIS. Out of the 6953 flares of GOES magnitude C1 or greater that we consider over the 6.5 years after the launch of SDO, 40 have been observed by six or more instruments simultaneously. Using each instrument's individual rate of success in observing flares, we show that the numbers of flares co-observed by three or more instruments are higher than the number expected under the assumption that the instruments operated independently of one another. In particular, the number of flares observed by larger numbers of instruments is much higher than expected. Our study illustrates that these missions often acted in cooperation, or at least had aligned goals. We also provide details on an interactive widget now available in SSWIDL that allows a user to search for flaring events that have been observed by a chosen set of instruments. This provides access to a broader range of events in order to answer specific science questions. The difficulty in scheduling coordinated observations for solar-flare research is discussed with respect to instruments projected to begin operations during Solar Cycle 25, such as DKIST, Solar Orbiter, and Parker Solar Probe.

Solar flares are events of intense scientific interest. Although certain solar conditions are known to be associated with flare activity, the exact location and timing of an individual flare on the Sun cannot as yet be predicted with certainty. Missions whose science objectives depend on observing solar flares must often make difficult decisions on where to target their observations if they do not observe the full solar disk. Yet, little analysis exists in the literature which might guide these missions' operations to maximize their opportunities to observe flares. In this study we analyze and simulate the performance of different observation strategies using historical flare and active region data from 2011 to 2014. We test a number of different target selection strategies based on active region complexity and recent flare activity, each of which is examined under a range of operational assumptions. In each case we investigate various metrics such as the number of flares observed, the size of flares observed, and operational considerations such as the number of instrument re-points that are required. Overall, target selection methods based on recent flare activity showed the best overall performance, but required more repointings than other methods. The mission responsiveness to new information is identified as a strong factor determining flare observation performance. It is also shown that target selection methods based on active region complexities show a significant pointing bias towards the western solar hemisphere. The number of flares observed grows quickly with field-of-view size until the approximate size of an active region is reached, but further improvements beyond the active region size are much more incremental. These results provide valuable performance estimates for a future mission focused on solar flares, and inform the requirements that would ensure mission success.

The SunPy Project is a community of scientists and software developers creating an ecosystem of Python packages for solar physics. The project includes the sunpy core package as well as a set of affiliated packages. The sunpy core package provides general purpose tools to access data from different providers, read image and time series data, and transform between commonly used coordinate systems. Affiliated packages perform more specialized tasks that do not fall within the more general scope of the sunpy core package. In this article, we give a high-level overview of the SunPy Project, how it is broader than the sunpy core package, and how the project curates and fosters the affiliated package system. We demonstrate how components of the SunPy ecosystem, including sunpy and several affiliated packages, work together to enable multi-instrument data analysis workflows. We also describe members of the SunPy Project and how the project interacts with the wider solar physics and scientific Python communities. Finally, we discuss the future direction and priorities of the SunPy Project.