Automated Coronal Hole Detection using Local Intensity Thresholding Techniques

We identify coronal holes using a histogram-based intensity thresholding technique and compare their properties to fast solar wind streams at three different points in the heliosphere. The thresholding technique was tested on EUV and X-ray images obtained using instruments onboard STEREO, SOHO and Hinode. The full-disk images were transformed into Lambert equal-area projection maps and partitioned into a series of overlapping sub-images from which local histograms were extracted. The histograms were used to determine the threshold for the low intensity regions, which were then classified as coronal holes or filaments using magnetograms from the SOHO/MDI. For all three instruments, the local thresholding algorithm was found to successfully determine coronal hole boundaries in a consistent manner. Coronal hole properties extracted using the segmentation algorithm were then compared with in situ measurements of the solar wind at 1 AU from ACE and STEREO. Our results indicate that flux tubes rooted in coronal holes expand super-radially within 1 AU and that larger (smaller) coronal holes result in longer (shorter) duration high-speed solar wind streams

The distribution of nearby stars in phase space mapped by Hipparcos III. Clustering and streaming among A-F type stars

This paper presents the detailed results obtained in the search of density- velocity inhomogeneities in a volume limited and absolute magnitude limited sample of A-F type dwarfs within 125 parsecs of the Sun. A 3-D wavelet analysis is used to extract inhomogeneities, both in the density and velocity distributions. Having established a real picture of the phase space without assumption we come back to previously known observational facts regarding clusters and associations, superclusters. In the 3-D position space, well known open clusters (Hyades, Coma Berenices and Ursa Major), associations (parts of the Scorpio-Centaurus association) as well as the Hyades evaporation track are retrieved. Three new probably loose clusters are identified (Bootes, Pegasus 1 and 2). The sample is relatively well mixed in the position space since less than 7 per cent of the stars belong to structures with coherent kinematics, most likely gravitationally bound. In the velocity space, the majority of large scale velocity structures ($\sigma$ ~ 6.3 $km s-1$) are Eggen's superclusters (Pleiades SCl, Hyades SCl and Sirius SCl) with the whole Centaurus association. A new supercluster-like structure is found with a mean velocity between the Sun and Sirius SCl velocities. These structures are all characterized by a large age range which reflects the overall sample age distribution. Moreover, a few old streams of ~ 2 Gyr are also extracted at this scale with high U components. We show that all these large velocity dispersion structures represent 46% of the sample. Smaller scales (\sigma ~ 3.8 and 2.4 $km s-1$) reveal that superclusters are always substructured by 2 or more streams which generally exhibit a coherent age distribution. Percentages of stars in these streams are 38% and 18% respectively.Comment: 25 pages, Latex, 29 figures, 4 tables to be published in A&A Supplements Serie

RLFC: Random Access Light Field Compression using Key Views and Bounded Integer Encoding

We present a new hierarchical compression scheme for encoding light field images (LFI) that is suitable for interactive rendering. Our method (RLFC) exploits redundancies in the light field images by constructing a tree structure. The top level (root) of the tree captures the common high-level details across the LFI, and other levels (children) of the tree capture specific low-level details of the LFI. Our decompressing algorithm corresponds to tree traversal operations and gathers the values stored at different levels of the tree. Furthermore, we use bounded integer sequence encoding which provides random access and fast hardware decoding for compressing the blocks of children of the tree. We have evaluated our method for 4D two-plane parameterized light fields. The compression rates vary from 0.08 - 2.5 bits per pixel (bpp), resulting in compression ratios of around 200:1 to 20:1 for a PSNR quality of 40 to 50 dB. The decompression times for decoding the blocks of LFI are 1 - 3 microseconds per channel on an NVIDIA GTX-960 and we can render new views with a resolution of 512X512 at 200 fps. Our overall scheme is simple to implement and involves only bit manipulations and integer arithmetic operations.Comment: Accepted for publication at Symposium on Interactive 3D Graphics and Games (I3D '19

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