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In Fall 2008 NASA selected a large international consortium to produce a comprehensive automated feature-recognition system for the Solar Dynamics Observatory (SDO). The SDO data that we consider are all of the Atmospheric Imaging Assembly (AIA) images plus surface magnetic-field images from the Helioseismic and Magnetic Imager (HMI). We produce robust, very efficient, professionally coded software modules that can keep up with the SDO data stream and detect, trace, and analyze numerous phenomena, including flares, sigmoids, filaments, coronal dimmings, polarity inversion lines, sunspots, X-ray bright points, active regions, coronal holes, EIT waves, coronal mass ejections (CMEs), coronal oscillations, and jets. We also track the emergence and evolution of magnetic elements down to the smallest detectable features and will provide at least four full-disk, nonlinear, force-free magnetic field extrapolations per day. The detection of CMEs and filaments is accomplished with Solar and Heliospheric Observatory (SOHO)/ Large Angle and Spectrometric Coronagraph (LASCO) and ground-based Hα data, respectively. A completely new software element is a trainable feature-detection module based on a generalized image-classification algorithm. Such a trainable module can be used to find features that have not yet been discovered (as, for example, sigmoids were in the pre- Yohkoh era). Our codes will produce entries in the Heliophysics Events Knowledgebase (HEK) as well as produce complete catalogs for results that are too numerous for inclusion in the HEK, such as the X-ray bright-point metadata. This will permit users to locate data on individual events as well as carry out statistical studies on large numbers of events, using the interface provided by the Virtual Solar Observatory. The operations concept for our computer vision system is that the data will be analyzed in near real time as soon as they arrive at the SDO Joint Science Operations Center and have undergone basic processing. This will allow the system to produce timely space-weather alerts and to guide the selection and production of quicklook images and movies, in addition to its prime mission of enabling solar science. We briefly describe the complex and unique data-processing pipeline, consisting of the hardware and control software required to handle the SDO data stream and accommodate the computer-vision modules, which has been set up at the Lockheed-Martin Space Astrophysics Laboratory (LMSAL), with an identical copy at the Smithsonian Astrophysical Observatory (SAO).

We detail the investigation of the first application of several dissimilarity measures for large-scale solar image data analysis. Using a solar-domain-specific benchmark dataset that contains multiple types of phenomena, we analyzed combinations of image parameters with different dissimilarity measures to determine the combinations that will allow us to differentiate between the multiple solar phenomena from both intra-class and inter-class perspectives, where by class we refer to the same types of solar phenomena. We also investigate the problem of reducing data dimensionality by applying multi-dimensional scaling to the dissimilarity matrices that we produced using the previously mentioned combinations. As an early investigation into dimensionality reduction, we investigate by applying multidimensional scaling (MDS) how many MDS components are needed to maintain a good representation of our data (in a new artificial data space) and how many can be discarded to enhance our querying performance. Finally, we present a comparative analysis of several classifiers to determine the quality of the dimensionality reduction achieved with this combination of image parameters, similarity measures, and MDS.

We present a comparative evaluation for automated filament detection in Hα solar images. By using metadata produced by the Advanced Automated Filament Detection and Characterization Code (AAFDCC) module, we adapted our trainable feature recognition (TFR) module to accurately detect regions in solar images containing filaments. We first analyze the AAFDCC module’s metadata and then transform it into labeled datasets for machine-learning classification. Visualizations of data transformations and classification results are presented and accompanied by statistical findings. Our results confirm the reliable event reporting of the AAFDCC module and establishes our TFR module’s ability to effectively detect solar filaments in Hα solar images.

This work investigates the applicability of several dimensionality reduction techniques for large-scale solar data analysis. Using a solar benchmark dataset that contains images of multiple types of phenomena, we investigate linear and nonlinear dimensionality reduction methods in order to reduce our storage and processing costs and maintain a good representation of our data in a new vector space. We present a comparative analysis of several dimensionality reduction methods and different numbers of target dimensions by utilizing different classifiers in order to determine the degree of data dimensionality reduction that can be achieved with these methods, and to discover the method that is the most effective for solar images. After determining the optimal number of dimensions, we then present preliminary results on indexing and retrieval of the dimensionally reduced data.

Decaying active region 10942 is investigated from 4:00 – 16:00 UT on 24 February 2007 using a suite of EUV observing instruments. Results from Hinode /EIS, STEREO and TRACE show that, although the active region has decayed and no sunspot is present, the physical mechanisms that produce distinguishable loop structures, spectral line broadening, and plasma flows still occur. A coronal loop that appears as a blue-shifted structure in Doppler maps is apparent in intensity images of log( T )=6.0 – 6.3 ions. The loop structure is found to be anti-correlated with spectral line broadening generally attributed to non-thermal velocities. This coronal loop structure is investigated physically (temperature, density, geometry) and temporally. Light curves created from imaging instruments show brightening and dimming of the loop structure on two different time scales; short pulses of 10 – 20 min and long duration dimming of two – four hours until its disappearance. The coronal loop structure, formed from relatively blue-shifted material that is anti-correlated with spectral line broadening, shows a density of 10 10 to 10 9.3  cm −3 and is visible for longer than characteristic cooling times. The maximum non-thermal spectral line broadenings are found to be adjacent to the footpoint of the coronal loop structure.

The structure of electric current and magnetic helicity in the solar corona is closely linked to solar activity over the 11-year cycle, yet is poorly understood. As an alternative to traditional current-free \"potential-field\" extrapolations, we investigate a model for the global coronal magnetic field which is non-potential and time-dependent, following the build-up and transport of magnetic helicity due to flux emergence and large-scale photospheric motions. This helicity concentrates into twisted magnetic flux ropes, which may lose equilibrium and be ejected. Here, we consider how the magnetic structure predicted by this model - in particular the flux ropes - varies over the solar activity cycle, based on photospheric input data from six periods of cycle23. The number of flux ropes doubles from minimum to maximum, following the total length of photospheric polarity inversion lines. However, the number of flux rope ejections increases by a factor of eight, following the emergence rate of active regions. This is broadly consistent with the observed cycle modulation of coronal mass ejections, although the actual rate of ejections in the simulation is about a fifth of the rate of observed events. The model predicts that, even at minimum, differential rotation will produce sheared, non-potential, magnetic structure at all latitudes.

The unresolved corona for three active regions (ARs) above the solar limb is investigated. Intensities measured for ions formed above 1 MK are presented as a function of height above the solar surface. The observed decrease in intensity with altitude is found to be best fit by an exponential. Furthermore, this exponential decrease is approximately the decrease in emission expected for a hydrostatic planar geometry atmosphere, where the scale height temperature is dependent on the dynamics of the AR. For two of the ARs analyzed, we have found that the best-fit temperature derived from the exponential fits is consistent with an isothermal hydrostatic unresolved corona.

We use an innovative research technique to analyze combined images from the Coronal Diagnostic Spectrometer (CDS) on the Solar and Heliospheric Observatory (SOHO) and the Transition Region and Coronal Explorer (TRACE). We produce a high spatial and temporal resolution simulated CDS raster or “composite” map from TRACE data and use this composite map to jointly analyze data from both instruments. We show some of the advantages of using the “composite” map method for coronal loop studies. We investigate two postflare loop structures. We find cool material (250 000 K) concentrated at the tips or apex of the loops. This material is found to be above its scale height and therefore not in hydrostatic equilibrium. The exposure times of the composite map and TRACE images are used to give an estimate of another loop’s cooling time. The contribution to the emission in the TRACE images for the spectral lines present in its narrow passband is estimated by using the CDS spectral data and CHIANTI to derive synthetic spectra. We obtain cospatial and cotemporal data collected by both instruments in SOHO Joint Observations Program (JOP) 146 and show how the combination of these data can be utilized to obtain more accurate measurements of coronal plasmas than if analyzed individually.