ISSI International Team on Mining and exploiting the NASA Solar Dynamics Observatory data in Europe

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1 ISSI International Team on Mining and exploiting the NASA Solar Dynamics Observatory data in Europe Minutes of Meeting ISSI, 30 May -1 June 2012, 3rd meeting Participants: Karel Schrijver, Paul Higgins, Alec Engell, Fraser Watson, Roman Brajsa, Eric Buchlin, Davina Innes, Cis Verbeeck, Luis Vieira, Rami Qahwaji, Stéphane Régnier, Véronique Delouille Summary During this last meeting we reviewed the algorithms that were developed for automated extraction and analysis of: sunspots, magnetic active regions, polarity inversion lines, coronal active regions, filaments, and coronal holes. We also discussed their application on SDO data. Some emphasis were made on analysing the flare-productive NOAA Active region. Solar irradiance reconstruction, and image processing tools for visualization of data were also presented. Presentations are available at html (username: solyneuro, password: NaSa_* ) Contents 1 Active regions through the lenses of SDO Karel Schrijver Sympathetic events Analysis of AR (South hemisphere Feb 10-Feb 15, 2011) Fraser Watson: Fragmentation of magnetic active regions - results from AR Davina Innes: SDO/AIA observations of EUV sunspot jets and associated type III burst Stéphane Régnier: Magnetic energy evolution around the X2 flare on 15 Feb Rami Qhawaji: Magnetic complexity analysis of AR11158 using the Ising Model Cis Verbeeck: AR11158 through SPoCA lenses Long-term analysis of solar features Roman Brasja: Proper motion of coronal bright points with EIT data Cis Verbeeck: A multi-wavelength analysis of active regions and sunspots by comparison of automatic detection algorithms

2 2.3 Paul Higgins: Magnetic features and global field over cycle Filaments, PIL, and Coronal Holes Eric Buchlin: Automated detection of filaments and their eruptions from SDO data Alexander Engell: Polarity inversion line maps with SDO-HMI Véronique Delouille: Coronal holes study on the first year of SDO-AIA data Spectral irradiance reconstruction (Luis Vieira) 6 5 Image processing for visualization (Rami Qahwaji) D Modelling of coronal loops Synoptic maps, super-resolved images HEK: search and browsing tool 7 7 Suggestions for future works Growing phase of an active region Decay phase of an active region Active regions through the lenses of SDO 1.1 Karel Schrijver Sympathetic events Sympathetic events were already suggested in Richardson (1951). Many simultaneous events are observed by AIA, and it is possible to search for them using the HEK catalog. For example, the August 1, 2010 event connected four events in 24h. The sites of eruption were connected through the skeleton of the magnetic field (null points and separators), see Schrijver & Title (2011). Thus in order to try and predict eruptions, we should observe the whole Sun, not just the Earth-facing side. MHD modeling of sympathetic flares is still something to be developed Analysis of AR (South hemisphere Feb 10-Feb 15, 2011) This active region contains multiple bi-poles. We can observe an expanding set of loop as well as an expanding front. This front is mostly brightening in 19.3nm and dimming in 17.1nm. Coronal loops, front, and CME (as seen in LASCO) are all well aligned to the same volume. The structure is accelerating, and the expansion is bounded by the helmet structure from the PFSS model. A possible study for CACTUS is to see if the opening angles of CMEs have something to do with PFSS helmet directions. See Schrijver et al. (2011) 2

3 1.2 Fraser Watson: Fragmentation of magnetic active regions - results from AR11158 Specific cadence: 10 Feb 2011 : 15 min cadence - 96 images MB Feb 2011 : 1 hour cadence images MB 15 Feb 2011 : 01:30-02:30 - highest available cadence (45 secs for HMI) - 80 images MB Small magnetic fragments are detected by a method that identifies the strongest pixels and use a watershed type algorithm (downhill algo) to delineate the fragments. Fragments are detected within AR11158 from February 10 till 16 (when AR reaches 60 degrees longitude). Number of fragments per frame rises linearly till Feb 13, then slope goes up, till midday Feb 14, when it reaches a high plateau. Number of fragments is more stable in SDO-HMI than in SOHO-MDI because of the increase in resolution. At around 18:00 on February 12th the slope steepens in all graphs. The northern black polarity has an emergent flux at that time. The region also gets a lot more negative at this time. During the period of intense flaring, no particular feature is seen in the number of fragments, area, magnetic field, absolute magnetic field. Future work: compare these results with other parameter, e.g. time series of absolute magnetic flux. Study of fragment motion: most splitting and movement is seen during the decay phase 1.3 Davina Innes: SDO/AIA observations of EUV sunspot jets and associated type III burst Type III bursts are the most frequently observed solar radio burst. The present study uses WAVES/WIND, SWAVES/STEREO ( MHz), and SDO data. It is observed that the emission drift from high to low frequency as electrons travel outward along open magnetic field lines through the corona and interplanetary medium. These electrons excite Langmuir waves at the electron plasma frequency f that are converted into radio waves : f = n e where n e is the electron density. In the analysis of AR on 3 Aug 2010, time difference movies of 193, 304, 211A, and HMI magnetograms exhibit periodic waves emanating from sunspot. Periodic jets are also observed. The radio bursts correlate very well with the EUV jets. The EUV jet emission also correlates well with brightening at what looks like their footpoint at the edge of the umbra. The jet emission lags the radio signals and the footpoint brightening by about 30s because the EUV jets take time to develop. For min after strong EUV jets are ejected, the footpoint brightens at roughly 3 min intervals. In both the EUV images and the extracted light curves, it looks as though the brightening is related to the 3-min sunspot oscillations, although the correlation coefficient is rather low. The only open field near the jets is rooted in the sunspot. Conclusions: Active region EUV/X-ray jets and interplanetary electron streams originate on the edge of the sunspot umbra. They form along a current sheet between the sunspot open field and closed field connecting to underlying satellite flux. Sunspot running penumbral waves cause roughly 3-min jet footpoint brightening. The relationship between the waves and jets is less clear, see Innes et al. (2011) 3

4 1.4 Stéphane Régnier: Magnetic energy evolution around the X2 flare on 15 Feb Two types of magnetic field extrapolations are combined in Régnier (2012): Potential Field Source Surface: it is the minimum of magnetic energy for a given distribution of B n (requires only B n from observations, fast computation) The open magnetic field: maximum of energy for force-free field Aly (1984, 1988). The difference between both energies gives upper limit of free magnetic energy. On the study of 15 Feb 2011 event, these two energies show time series very much like Fraser s flux time series. The energies started to decrease 5h before the flare, and at the flare time there is a local minimum. 1.5 Rami Qhawaji: Magnetic complexity analysis of AR11158 using the Ising Model The Ising model is designed to analyze magnetic interactions and structures related to ferromagnetic substances. It enables the simplification of complex interactions, and has been successfully applied to many physical systems including: magnetism, liquid-gas transition, etc. The Ising Magnetic Complexity (IMC) model (Ahmed et al. 2010) is used to estimate the magnetic complexity in AR from magnetogram images. The image is mapped to a 2D matrix, where negative polarity is -1, postitive polarity is +1, and neutral (mid-gray value) is 0. Paul Higgins on the other hand uses the actual B values. Results on various flaring regions indicate that flaring happens when Ising energy reaches above 1. When IMC reaches 1.5, X flares are produced. The aim of a time series analysis in this case would be to identify the evolution of magnetic features and properties associated. Often some preprocessing is necessary: remove seasonality, or trend. Note though that a substantial amount of ARs needs to be studied to find out a consistent pattern. Other work related to flare prediction: a short-term solar flare prediction model using predictor teams rather than an individual set of predictors is introduced in Huang et al. (2010). The predictors used in this study are: maximum horizontal gradient, length of neural line, and the number of singular points. Ahmed et al. (2011) evaluates the flare-prediction-capability of magnetic feature (MF) properties generated by Solar Monitor Active Region Tracker (SMART) and described in Higgins et al. (2011). 1.6 Cis Verbeeck: AR11158 through SPoCA lenses Cis describes a first analysis of evolution of AR11158 using SPOCA. Positions are stable, but areas and intensities fluctuate a lot. Davina suggests a solarsoft routine (browse-sphere) that merges SDO and STEREO data in a Carrington map. We could take this as an input image for SPoCA in order to avoid limb projection effects. See Typical behavior of a decaying AR: flux decays, but area increases as it spreads out One problem is to generate stable time series using SPoCA. Even if two ARs do connect in reality, we would like to just focus on the part that corresponds to the AR we were originally following. Magnetogram info, or even PFSS extrapolations could help in this. Or, if we use a sufficiently high cadence, we might put an upper limit on the time derivative of the area. We could use a common bounding box as given by SMART. 4

5 2 Long-term analysis of solar features 2.1 Roman Brasja: Proper motion of coronal bright points with EIT data Three methods are investigated: interactive visual method, semi-automatic method (5x faster), automatic method. The Bright Points cover almost all latitudes. This study allows for a precise modeling of differential rotation and meridional flow. The computation of differential rotation coefficients with the automatic method provides less stable results than those of the interactive visual method, because we lose some information/control. 2.2 Cis Verbeeck: A multi-wavelength analysis of active regions and sunspots by comparison of automatic detection algorithms In Verbeeck et al. (2011), four algorithms presented in this ISSI team where compared: SMART extracts, characterises, and tracks the evolution of active regions across the solar disk using line-of-sight magnetograms and a combination of image-processing techniques. ASAP converts continuum images from heliocentric coordinates to Carrington heliographic coordinates, detects and tracks sunspots and pores using thresholding and morphological methods. STARA is used to detect and track sunspots from continuum images using a technique known as the top-hat transform. SPoCA segments solar EUV images into active regions (AR), coronal holes (CH) and quiet Sun (QS). It is used here to detect, characterise, and track coronal active regions. The overall performance of the algorithms is compared by determining the total number of features detected as well as their full-disk area. The methods are benchmarked against NOAA and Solar Influences Data Analysis Centre (SIDC) catalogues. Correlations between the properties determined by the algorithms are investigated using Principal Component Analysis. PCA 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 (4 June 2003) and (19 May 2003) 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. 2.3 Paul Higgins: Magnetic features and global field over cycle 23 Paul presented a study of magnetic field over Solar Cycle 23: number of active regions in both hemisphere, distribution of area, distribution of flux (rising phase, plateau, declining phase). In the butterfly diagram, Paul detects AR up to a bit higher latitudes than sunspots (smaller features). They also separated the global field into a set of harmonic modes. In the solution of the Potential Field Source Surface Model, they look for axisymmetric and hemispherically antisymmetrical solutions. 5

6 3 Filaments, PIL, and Coronal Holes 3.1 Eric Buchlin: Automated detection of filaments and their eruptions from SDO data Filaments are prominences viewed on the disk, in absorption. They represent cool, dense (chromospheric) material maintained in equilibrium in the corona thanks to the magnetic field. This equilibrium can become unstable and filament plasma can be ejected in interplanetary space as CME. Several codes exist for detecting filament in H-alpha. In He-II, the detection is more difficult than in H-alpha, but the high AIA resolution is an advantage, as well as the lack of clouds. Method: Curvelets were not ideal for detection of filament. Eric now uses a measure of local uniformity computed from local intensity histograms: Pixels are clustered according to intensity in filtered image. Scores are attributed according to size, brightness in original and filtered image (eliminate bright features), shape (ratio to circle of same diameter), position (eliminate features too close to limb), and also proximity to PIL in order to eliminate CH. Eric also detects filaments with highly oscillating subspine directions. These could be pre-filament canopies radiating from an AR as described in Wang et. al. Wang et al. (2011). 3.2 Alexander Engell: Polarity inversion line maps with SDO-HMI The algorithm to find Polarity Inversion line maps is introduced in Martens et al. (2012). Some remarks: There are a lot of flickering in PIL, but PIL in strong AR are stable. Vector magnetograms have a 180 degrees ambiguity. Need to work on that. In Falconer et al. (2009), they look at log-log plots of flare rates versus free magnetic energy proxy. This could give better results than the McIntosh-based flare prediction. Naming of PIL: Alec uses Michael Turmon s magnetic AR detection which provides NOAA naming, and names PIL inside each of these AR bounding boxes. 3.3 Véronique Delouille: Coronal holes study on the first year of SDO-AIA data Using SPoCA, detection and extraction of coronal holes was applied on one year of SDO data (June 2010, May 2011). We use 193A bandpass, and apply the algorithm on the square root of the image (reduction of Poisson noise). The results for January 2011 reveals a lot of short-lived CH candidate. We use HMI to check the quality of detection. For CH that lives longer than 5 days, statistics of HMI are consistent with physics of CH being unipolar regions: the sign of the mean and skewness of magnetic field as measured by HMI within a coronal hole is constant over time. Statistics and position (chain code) of coronal holes as found by SPOCA can be retrieved from the HEK, see spoca_hek. 4 Spectral irradiance reconstruction (Luis Vieira) Solar spectral irradiance is important for understanding long-term Earth climate Going back to the past, we have observations of different nature: electronic, photographic, sunspot observations, naked eye 6

7 sunspot observations, cosmogenic isotopes. Sunspots lead to a TSI darkening, whereas faculae leads to TSI brightening. Both effects are in phase with the solar cycle. Since the facula brightening effect is more important than the sunspot darkening, TSI is in phase with the solar cycle. The SATIRE-S model Krivova et al. (2006) attributes to each of the pixels in magnetogram and continuum image a certain behavior based on the pixel s class (faculae, quiet Sun, umbra, penumbra, network). The TSI and SSI reconstruction that uses only sunspot numbers works by estimating the time derivatives of AR, ephemeral, open and slow open flux based on sunspot number evolution. The TSI and SSI reconstruction with only cosmic ray data must use a 10-year-averaged C14 data. Models exist to estimate the open flux and the other parameters. Over the period 1000 BC AD, variation of TSI is only W/m 2. We are now in an absolute maximum. Comparison between C14 and Beryllium TSI results: same range of variability, deviations up to 0.5 W/m 2. For near real-time reconstruction of TSI/SSI, magnetogram and continuum image are used. AR and ephemeral regions are identified and filling factor computed. Luis uses a neural network model, trained on SORCE data. The resulting TSI and SSI corresponds well with SORCE data, see for the nowcast of SSI. The model fails below 100 nm, which is why it would be useful to include CH and perhaps coronal AR and QS in his model. All of these reconstructions correspond to measurements as relative variations, not in absolute values. Even different TSI measurements disagree on the absolute values but agree on the relative variations. 5 Image processing for visualization (Rami Qahwaji) In Colak et al. (2011), the team of U. Bradford describes how to represent solar features in 3D for creating visual solar catalogues. Most recent developments are as follows: 5.1 3D Modelling of coronal loops Existing methods can be categorized in three groups: 1. Stereoscopic triangulation method. 2. Magneto-Hydro-Dynamics (MHD) model. : Complex set of equations, that are difficult/expensive to solve. It can be used to describe global solar magnetic loops, but because of complexity it is more suitable for bounded regions. 3. Potential Field Source Surface (PFSS) model: Simple to implement and solutions converge rapidly. Problem: there is no time dependence. The approach proposed by U. Bradford proceeds as follows: First, build synoptic maps using MDI or HMI. Second, detect magnetic footprints. Then perfrom a nearest neighbor search and look for the optimal association between magnetic footpoint pairs. The associated footpoints are connected by loops in a 3D synoptic maps. Comparison with PFSS results and images are good. The procedure is almost fully automatic and is Matlab-based. 5.2 Synoptic maps, super-resolved images Other tools developed at U. Bradford include the construction of synoptic maps using MDI or HMI, and the super resolution (SR), see Zraqou et al. (2009). With super-resolution techniques, a set of small 7

8 satellite images taken at high cadence can be post-processed and transformed into an image having finer details. 6 HEK: search and browsing tool The graphical interface of the HEK is available at It allows to browse data to see when events do occur. Description of keywords for all VO Events recorded in the HEK are available at hek/voevent_spec.html. There are now lots events recorded in Isolsearch, so there is a need to cluster events into meta-events. SPOCA feeds the HEK with properties of AR and CH. If you are interested in large data sets of AR or CH (for example), it is preferable to use the ontology package with SSW/IDL. See oma.be/web/sdoatsidc/spoca_hek for some instructions on how to use SPOCA within the HEK. 7 Suggestions for future works During the last day, we discussed some ideas for future works. 7.1 Growing phase of an active region NOAA is observed by SDO from its birth. Hence it is possible to study its evolution in different phases: emergence, first growth, then sudden growth at an increased rate. In practice, we could proceed as follows: Provide a common bounding box given by SMART: wait until AR has completely developed, take the SMART bounding box and rotate it back to have a fixed size frame, then study property of MF from a few days before emergence. Put the SMART mask in all wavelengths (and DEM maps) Follow AR over 3 rotations, using STEREO browse-sphere As AR decays, its constituent flux can move poleward and generate CH Various elements of the growing phase could be studied together: Magnetic fragments (Fraser): area, unbalanced observed magnetic flux, study of interaction with existing magnetic field Properties of SMART (Paul) and Ising magnetic complexity (Rami) Magnetic energy (Stephane) Intensity variations in EUV images (SPoCA, jets) Type III radio burst (Davina): from which part (sunspot, AR merged) do the Type III radio bursts come from. What is their relation with open magnetic field. Link with change in spectral irradiance (Luis): SSI reconstruction, estimated every six hours, one observation per day of SORCE. 8

9 7.2 Decay phase of an active region The decay of AR happens over 3 rotations, with polarities drifting apart and coronal hole emerging near the decaying AR. One can also observe the emergence of a filament after 3 rotations. We can use STEREO tool to have the 360 view and try to answer questions such as: What causes the growth of the filament? How does the flux transfer from active regions to coronal holes? References Ahmed, O., Qahwaji, R., Colak, T., Dudok De Wit, T., & Ipson, S. 2010, The Visual Computer, 26, 385 Ahmed, O. W., Qahwaji, R., Colak, T., et al. 2011, Sol. Phys., 404 Aly, J. J. 1984, ApJ, 283, 349 Aly, J. J. 1988, A&A, 203, 183 Colak, T., Qahwaji, R., Ipson, S., & Ugail, H. 2011, Advances in Space Research, 47, 2092 Falconer, D. A., Moore, R. L., Gary, G. A., & Adams, M. 2009, ApJ, 700, L166 Higgins, P. A., Gallagher, P. T., McAteer, R. T. J., & Bloomfield, D. S. 2011, Advances in Space Research, 47, 2105 Huang, X., Yu, D., Hu, Q., Wang, H., & Cui, Y. 2010, Sol. Phys., 263, 175 Innes, D. E., Cameron, R. H., & Solanki, S. K. 2011, A&A, 531, L13 Krivova, N. A., Solanki, S. K., & Floyd, L. 2006, A&A, 452, 631 Martens, P. C. H., Attrill, G. D. R., Davey, A. R., et al. 2012, Sol. Phys., 275, 79 Régnier, S. 2012, Sol. Phys., 277, 131 Richardson, R. S. 1951, ApJ, 114, 356 Schrijver, C. J., Aulanier, G., Title, A. M., Pariat, E., & Delannée, C. 2011, ApJ, 738, 167 Schrijver, C. J. & Title, A. M. 2011, Journal of Geophysical Research (Space Physics), 116, 4108 Verbeeck, C., Higgins, P. A., Colak, T., et al. 2011, Sol. Phys., 369 Wang, Y.-M., Robbrecht, E., & Muglach, K. 2011, ApJ, 733, 20 Zraqou, J., Alkhadour, W., Qahwaji, R., Ipson, S., & Ugail, S. 2009, UbiCC Journal, 4, 1 9