Observations of Detailed Structure in the Solar Wind at 1 AU with STEREO/HI-2

Heliospheric imagers offer the promise of remote sensing of large-scale structures present in the solar wind. The STEREO/HI-2 imagers, in particular, offer high resolution, very low noise observations of the inner heliosphere but have not yet been exploited to their full potential. This is in part because the signal of interest, Thomson scattered sunlight from free electrons, is ~1000 times fainter than the background visual field in the images, making background subtraction challenging. We have developed a procedure for separating the Thomson-scattered signal from the other background/foreground sources in the HI-2 data. Using only the Level 1 data from STEREO/HI-2, we are able to generate calibrated imaging data of the solar wind with sensitivity of a few times 1e-17 Bsun, compared to the background signal of a few times 1e-13 Bsun. These images reveal detailed spatial structure in CMEs and the solar wind at projected solar distances in excess of 1 AU, at the instrumental motion-blur resolution limit of 1-3 degree. CME features visible in the newly reprocessed data from December 2008 include leading-edge pileup, interior voids, filamentary structure, and rear cusps. "Quiet" solar wind features include V shaped structure centered on the heliospheric current sheet, plasmoids, and "puffs" that correspond to the density fluctuations observed in-situ. We compare many of these structures with in-situ features detected near 1 AU. The reprocessed data demonstrate that it is possible to perform detailed structural analyses of heliospheric features with visible light imagery, at distances from the Sun of at least 1 AU.Comment: Accepted by Astrophysical Journa

DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels

The impact of soiling on solar panels is an important and well-studied problem in renewable energy sector. In this paper, we present the first convolutional neural network (CNN) based approach for solar panel soiling and defect analysis. Our approach takes an RGB image of solar panel and environmental factors as inputs to predict power loss, soiling localization, and soiling type. In computer vision, localization is a complex task which typically requires manually labeled training data such as bounding boxes or segmentation masks. Our proposed approach consists of specialized four stages which completely avoids localization ground truth and only needs panel images with power loss labels for training. The region of impact area obtained from the predicted localization masks are classified into soiling types using the webly supervised learning. For improving localization capabilities of CNNs, we introduce a novel bi-directional input-aware fusion (BiDIAF) block that reinforces the input at different levels of CNN to learn input-specific feature maps. Our empirical study shows that BiDIAF improves the power loss prediction accuracy by about 3% and localization accuracy by about 4%. Our end-to-end model yields further improvement of about 24% on localization when learned in a weakly supervised manner. Our approach is generalizable and showed promising results on web crawled solar panel images. Our system has a frame rate of 22 fps (including all steps) on a NVIDIA TitanX GPU. Additionally, we collected first of it's kind dataset for solar panel image analysis consisting 45,000+ images.Comment: Accepted for publication at WACV 201

Improvements on coronal hole detection in SDO/AIA images using supervised classification

We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection techniques (intensity-based thresholding, SPoCA), we prepared data sets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2011 - 2013. By mapping the extracted regions from EUV observations onto HMI line-of-sight magnetograms we also include their magnetic characteristics. We computed shape measures from the segmented binary maps as well as first order and second order texture statistics from the segmented regions in the EUV images and magnetograms. These attributes were used for data mining investigations to identify the most performant rule to differentiate between coronal holes and filament channels. We applied several classifiers, namely Support Vector Machine, Linear Support Vector Machine, Decision Tree, and Random Forest and found that all classification rules achieve good results in general, with linear SVM providing the best performances (with a true skill statistic of ~0.90). Additional information from magnetic field data systematically improves the performance across all four classifiers for the SPoCA detection. Since the calculation is inexpensive in computing time, this approach is well suited for applications on real-time data. This study demonstrates how a machine learning approach may help improve upon an unsupervised feature extraction method.Comment: in press for SWS

Narrow-line-width UV bursts in the transition region above Sunspots observed by IRIS

Various small-scale structures abound in the solar atmosphere above active regions, playing an important role in the dynamics and evolution therein. We report on a new class of small-scale transition region structures in active regions, characterized by strong emissions but extremely narrow Si IV line profiles as found in observations taken with the Interface Region Imaging Spectrograph (IRIS). Tentatively named as Narrow-line-width UV bursts (NUBs), these structures are located above sunspots and comprise of one or multiple compact bright cores at sub-arcsecond scales. We found six NUBs in two datasets (a raster and a sit-and-stare dataset). Among these, four events are short-living with a duration of $\sim$10 mins while two last for more than 36 mins. All NUBs have Doppler shifts of 15--18 km/s, while the NUB found in sit-and-stare data possesses an additional component at $\sim$50 km/s found only in the C II and Mg II lines. Given that these events are found to play a role in the local dynamics, it is important to further investigate the physical mechanisms that generate these phenomena and their role in the mass transport in sunspots.Comment: 8 pages, 4 figures and 1 table, accepted for publication in ApJ

Observational features of equatorial coronal hole jets

Collimated ejections of plasma called "coronal hole jets" are commonly observed in polar coronal holes. However, such coronal jets are not only a specific features of polar coronal holes but they can also be found in coronal holes appearing at lower heliographic latitudes. In this paper we present some observations of "equatorial coronal hole jets" made up with data provided by the STEREO/SECCHI instruments during a period comprising March 2007 and December 2007. The jet events are selected by requiring at least some visibility in both COR1 and EUVI instruments. We report 15 jet events, and we discuss their main features. For one event, the uplift velocity has been determined as about 200 km/s, while the deceleration rate appears to be about 0.11 km/s2, less than solar gravity. The average jet visibility time is about 30 minutes, consistent with jet observed in polar regions. On the basis of the present dataset, we provisionally conclude that there are not substantial physical differences between polar and equatorial coronal hole jets.Comment: 9 pages, 8 figures, 1 table, accepted for publication in Annales Geophysicae, Special Issue:'Three eyes on the Sun-multi-spacecraft studies of the corona and impacts on the heliosphere