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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. [ABSTRACT FROM AUTHOR]

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 to 10 cm 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. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

The two-dimensional Van Tend and Kuperus (1978) scenario for pre-flare energy build-up is extended to a fully three-dimensional model and applied to the 16 May, 1981 flare observed at Debrecen. It is shown that there is plenty of free energy (10 erg) available to explain the ensuing large two-ribbon flare. This estimate is an order of magnitude larger than the simple estimate made by Van Tend, as a result of the three-dimensional character of the present model. It is further confirmed that the global form of the preflare circuit is decisive for determining the amount of energy stored in the preflare configuration, while the internal structure of the filament is of little importance. This is in accordance with the similar claims of Alfvén and Van Tend and Kuperus. Order of magnitude estimates are derived for all the lumped circuit parameters of the preflare filament-return current circuit; self-inductance, resistance, current strength, and applied voltage. It is found that the model gives correct predictions for the independently observed photospheric flow velocity and current strength in filaments. [ABSTRACT FROM AUTHOR]