adsabs/AstroCLIMB huggingface dataset:
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Schematic representation of our U-Net architecture. It consists of an Encoder (left blue box) and a Decoder (right red box). The leftmost input is a 3D array combing three tone-mapped data, and the rightmost output is the prediction 3D array: prominences (green), ARs (red), and background (blue). The cubes represent feature maps, where the dimension of each map is indicated on its left, and the number of channels is indicated above it. Operations for each channel are represented by arrows: the blue arrows indicate a convolution operation with a 3 × 3 kernel followed by a LeakyReLu activation function; the pink arrows indicate a DropOut operation in addition to the operation indicated by the blue arrow; the red arrows represent a downsampling operation using max pooling with 2 × 2 pooling size; the green arrows indicate an upsampling operation by transpose convolution operation with a 3 × 3 kernel; the gray arrows indicate the array in Encoder splice to the array in Decoder. H and W refer to the height and width of each feature map, respectively.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Flowchart showing the processes of our detection system. Objects in the same yellow box represent some functionality and its associated output or products. Red arrows link the processes of prediction. Green arrows connect the steps of the model training. Blue arrows illustrate the parts extracted from the predicted results.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Accuracy and loss during training.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Butterfly diagram and latitudinal migration of prominences. (a) Butterfly diagram of the prominences from 2010 to 2020. Each dot represents a single prominence. The color bar shows the logarithm of the prominence areas. The green bands show the data used to calculate the drift velocities. (b) Temporal evolution of the monthly average latitude of the high-latitude prominences in the northern hemisphere, which is fitted with a cubic polynomial function indicated by the red line. (c) Fitted drift velocity variations of the high-latitude prominences in the northern hemisphere. Panels (d)–(e) are similar to panels (b) and (c) but for the high-latitude prominences in the southern hemisphere. Panels (f)–(i) are similar to panels (b)–(e) but for the low-latitude prominences.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Distribution of the prominence number from 2010 to 2020. (a) The total prominence number in each year. Panels (b) and (c) are similar to pane (a) but for the number of prominences that are grouped by areas and latitude bands, respectively.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Similar to Figure 9 but for the prominences with areas over 4000 Mm2.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Distribution of the prominence number with respect to latitude. (a) The normalized prominence number in different latitude bands. (b) Distribution of the prominence number within three area groups in different latitude bands.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Similar to Figure 9 but for the prominences with areas lower than 1000 Mm2.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Distributions of the prominences feature with respect to latitude. Panels (a), (b), and (c) show the average area, width, and height of the prominences in different latitude bands, respectively.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Examples of the IoU. The blue block and red block represent two sets, and the purple region represents where they intersect.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: IoU metrics of prominences and ARs by U-Net.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Similar to Figure 9 but for the prominences with areas within 1000–4000 Mm2.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Distribution of the AR number with respect to latitude. (a) The normalized AR number in the different latitude bands. (b) Distribution of the AR number within three area groups in different latitude bands.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Yearly asymmetries of the prominence feature with respect to latitude from 2010 to 2020. (a) Yearly asymmetry indices of the prominence numbers in different latitude bands. (b) Yearly asymmetry indices of the cumulative prominence areas in different latitude bands. The solid lines marked with circles, dashed lines marked with triangles, and dot lines marked with squares represent all prominences, low and high-latitude prominences, respectively.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Relationship between the thickness and width of the prominences. Each dot shows a single prominence. The blue and orange dots represent the observations in the southern and northern hemispheres, respectively. The right panel is the percentage profile with respect to the prominence thickness. The top panel is the percentage profile with respect to the prominence width. The blue and orange lines represent the results in the southern and northern hemispheres, respectively.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Distributions of the prominences feature with respect to years. Panels (a), (b), and (c) show the average area, width, and height of the prominences in different years, respectively.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Similar to Figure 13 but for the cumulative area.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Similar to Figure 15 but for the cumulative area.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Variation of the aspect ratio of the prominences with respect to latitude.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Variation of the aspect ratio of the prominences with respect to years.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Variations of the distance between the bottom of prominence and the solar limb.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Distribution of the AR number from 2010 to 2020. (a) The cumulative AR number in each year. Panels (b) and (c) are similar to panel (a) but for the number of ARs, which are grouped by areas and latitude bands, respectively.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Butterfly diagram and latitudinal migration of ARs. (a) Butterfly diagram of ARs from 2010 to 2020. Each dot represents a single AR. The color bar shows the logarithm of AR areas. The blue bands show the data used to calculate the drift velocities of the AR. (b) Temporal evolution of the monthly average latitude distributions of the AR from 2010 to 2020 in the northern hemisphere. Cubic polynomial fittings as shown as the red lines. (c) Drift velocity variations of the monthly average latitude distributions of the AR from 2010 to 2020 in the northern hemisphere. Panels (d) and (e) are similar to (b) and (c) but for the ARs in the southern hemisphere.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Relationship between the thickness and width of ARs. Each dot represents a single AR. The blue and orange dots represent the observations in the southern and northern hemispheres, respectively. The right panel is the percentage profile with respect to the thickness of the ARs. The top panel is the percentage profile with respect to the width of the ARs. The blue and orange lines represent the results in the southern and northern hemispheres, respectively.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Distributions of the AR features with respect to latitude. Panels (a), (b), and (c) show the average area, width, and height of the ARs in different latitude bands, respectively.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Distributions of the AR features with respect to years. Panels (a), (b), and (c) show the average area, width, and height of the ARs in different years, respectively.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Variation of the aspect ratio of the ARs with respect to latitude.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Variation of the aspect ratio of the ARs with respect to years.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Similar to Figure 25 but for the cumulative area.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Similar to Figure 27 but for the cumulative area.
Paper: Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning
Authors: T. Zhang, Q. Hao, P. F. Chen
Figure caption: Yearly asymmetries of the number (panel (a)) and cumulative area (panel (b)) of all ARs.
Paper: Presupernova Evolution and Explosive Nucleosynthesis of Rotating Massive Stars. II. The Supersolar Models at [Fe/H] = 0.3
Authors: Lorenzo Roberti, Marco Limongi, Alessandro Chieffi
Figure caption: Comparison between the [X/O] of all the elements in the 13, 15, 20, and 25 M ⊙ models of Set R, with solar metallicity (LC18, left panels) and with supersolar metallicity (this work, right panels). The different colors represent the different initial rotation velocities: 0 (black), 150 (red), and 300 km s−1 (blue). The horizontal dashed lines mark the ﹩{\mathrm{Log}}_{10}({\mathrm{PF}}_{{\rm{O}}})﹩ value corresponding to each initial rotation velocity.
Paper: Presupernova Evolution and Explosive Nucleosynthesis of Rotating Massive Stars. II. The Supersolar Models at [Fe/H] = 0.3
Authors: Lorenzo Roberti, Marco Limongi, Alessandro Chieffi
Figure caption: Comparison between the [X/O] ratios of the nonrotating models of Set R, with solar metallicity (LC18, red dots) and with supersolar metallicity (this work, blue) for the four masses 13, 15, 20, and 25 M ⊙. The horizontal solid line marks the [X/O]  =  0 value. The horizontal dashed lines refer to the ﹩-{\mathrm{Log}}_{10}({\mathrm{PF}}_{{\rm{O}}})﹩ value corresponding to each metallicity. The vertical black dotted line marks the atomic number of Sr.
Paper: Presupernova Evolution and Explosive Nucleosynthesis of Rotating Massive Stars. II. The Supersolar Models at [Fe/H] = 0.3
Authors: Lorenzo Roberti, Marco Limongi, Alessandro Chieffi
Figure caption: Comparison between SM (LC18, red) and SSM models (this work, blue) of key quantities. The different symbols represent the different initial rotation velocities: 0 (dots), 150 (squares), and 300 km s−1 (asterisks). Top-left panel: He core mass; top-right panel: CO core mass; bottom-left panel: final mass; bottom-right panel: 12C left by central He burning.
Paper: Presupernova Evolution and Explosive Nucleosynthesis of Rotating Massive Stars. II. The Supersolar Models at [Fe/H] = 0.3
Authors: Lorenzo Roberti, Marco Limongi, Alessandro Chieffi
Figure caption: Same as Figure 3, but for models with an initial rotation velocity v = 300 km s−1.
Paper: Presupernova Evolution and Explosive Nucleosynthesis of Rotating Massive Stars. II. The Supersolar Models at [Fe/H] = 0.3
Authors: Lorenzo Roberti, Marco Limongi, Alessandro Chieffi
Figure caption: Logarithm of the ratio between our nonrotating SSM elemental yields and those from Nomoto et al. (2013) in two selected cases: 13 M ⊙ (upper panel) and 20 M ⊙ (lower panel).
Paper: Presupernova Evolution and Explosive Nucleosynthesis of Rotating Massive Stars. II. The Supersolar Models at [Fe/H] = 0.3
Authors: Lorenzo Roberti, Marco Limongi, Alessandro Chieffi
Figure caption: Comparison between the evolution of SM and SSM models in the H-R diagram: the solid and dashed lines refer to the SSM and SM models, respectively.
Paper: CAMEL. II. A 3D Coronal Mass Ejection Catalog Based on Coronal Mass Ejection Automatic Detection with Deep Learning
Authors: Jiahui Shan, 家辉 单, Huapeng Zhang, 化鹏 张, Lei Lu, 磊 卢, Yan Zhang, 岩 张, Li Feng, 莉 封, Yunyi Ge, 蕴翊 葛, Jianchao Xue, 建朝 薛, Shuting Li, 舒婷 李
Figure caption: Demonstration of the CME tracking module. (a) The red part in the RDF image is the segmented pixel-level CME area. (b) Transformation of the red segmented CME regions to the gray regions. The red line represents 1.5R ⊙, the minimum height we defined. The red curve is obtained by fitting the maximum height at each PA. (c) CME height–time diagram at the PA with median velocity. For each blue triangle, the abscissa represents the time lapse of each image to the first image in the CME image sequence, while the ordinate represents the CME height at the PA. The yellow star denotes the position where an increase in height is strictly required. The red star indicates the fulfillment of our current rules, and all height points during this period are used for fitting the velocity. (d) The stack of CME detection, plotted in time, i.e., image number against PA measured counterclockwise from the solar north. Each row corresponds to an image. White dots within each row indicate outward propagation at these PAs in the subsequent frame. (e) Each red bounding box represents a divided image sequence with different colors. Small gaps are eliminated using a closing morphological operator. (f) Four distinct CME events were successfully separated from an image sequence. The four extracted frames of CME events are taken from the ending boundaries of the bounding boxes. These masks were overlaid onto the corresponding RDF images.
Paper: CAMEL. II. A 3D Coronal Mass Ejection Catalog Based on Coronal Mass Ejection Automatic Detection with Deep Learning
Authors: Jiahui Shan, 家辉 单, Huapeng Zhang, 化鹏 张, Lei Lu, 磊 卢, Yan Zhang, 岩 张, Li Feng, 莉 封, Yunyi Ge, 蕴翊 葛, Jianchao Xue, 建朝 薛, Shuting Li, 舒婷 李
Figure caption: The SegFormer segmentation model uses RDF images as input and predicts the structure of CMEs as output. SegFormer’s architecture primarily comprises two components: an encoder and a decoder. The Mix Transformer encoder consists of four different transformer blocks. There are three modules within each transformer block: overlap patch merging, ESA, and Mix-FFN. H represents the height of the input image, W denotes its width, and C  i (i ∈ 1, 2, 3, 4) is the number of channels in the output from each transformer block’s final dimension. The decoder consists of four MLP layers and predicts the semantic segmentation mask, where C is the number of channels and N class is the number of categories.
Paper: CAMEL. II. A 3D Coronal Mass Ejection Catalog Based on Coronal Mass Ejection Automatic Detection with Deep Learning
Authors: Jiahui Shan, 家辉 单, Huapeng Zhang, 化鹏 张, Lei Lu, 磊 卢, Yan Zhang, 岩 张, Li Feng, 莉 封, Yunyi Ge, 蕴翊 葛, Jianchao Xue, 建朝 薛, Shuting Li, 舒婷 李
Figure caption: Each annual bar chart displays the number of CME events recorded per month from 2010 to 2012. Dark blue rectangular bars for each month indicate the number of events matched with the COR1-A Preliminary CME List, while light blue rectangular bars indicate unmatched CME events.
Paper: CAMEL. II. A 3D Coronal Mass Ejection Catalog Based on Coronal Mass Ejection Automatic Detection with Deep Learning
Authors: Jiahui Shan, 家辉 单, Huapeng Zhang, 化鹏 张, Lei Lu, 磊 卢, Yan Zhang, 岩 张, Li Feng, 莉 封, Yunyi Ge, 蕴翊 葛, Jianchao Xue, 建朝 薛, Shuting Li, 舒婷 李
Figure caption: Flowchart for automatically reconstructing 3D CMEs.
Paper: CAMEL. II. A 3D Coronal Mass Ejection Catalog Based on Coronal Mass Ejection Automatic Detection with Deep Learning
Authors: Jiahui Shan, 家辉 单, Huapeng Zhang, 化鹏 张, Lei Lu, 磊 卢, Yan Zhang, 岩 张, Li Feng, 莉 封, Yunyi Ge, 蕴翊 葛, Jianchao Xue, 建朝 薛, Shuting Li, 舒婷 李
Figure caption: Our segmentation results vs. CAMEL on two CME events based on LASCO C2 images. From left to right: LASCO C2 RDF images, CAMEL method on LASCO C2, our segmentation results on LASCO C2. From top to bottom are three CME events at different times.
Paper: CAMEL. II. A 3D Coronal Mass Ejection Catalog Based on Coronal Mass Ejection Automatic Detection with Deep Learning
Authors: Jiahui Shan, 家辉 单, Huapeng Zhang, 化鹏 张, Lei Lu, 磊 卢, Yan Zhang, 岩 张, Li Feng, 莉 封, Yunyi Ge, 蕴翊 葛, Jianchao Xue, 建朝 薛, Shuting Li, 舒婷 李
Figure caption: Multiviewpoint visualization of CMEs in the 3D space. The left panel shows the positional relationship between the points of 3D CME reconstruction on the foreside and the Sun, the Earth, and STEREO-A. Red dots and blue dots indicate the CME evolution distribution at 19:05 and 19:35 on 2008 March 25, respectively. In the right top panel, blue dots represent 3D reconstructed points projected onto STEREO-A’s total brightness image. The right bottom panel is a top view of the left panel and the positions of the CME at two time moments on the other side are added.
Paper: A Deep Redshift Survey of the Perseus Cluster (A426): Spatial Distribution and Kinematics of Galaxies
Authors: Wooseok Kang, Ho Seong Hwang, Hyunmi Song, Changbom Park, Narae Hwang, Byeong-Gon Park
Figure caption: Observation fields of MMT/Hectospec in the Perseus cluster region. Dashed circles indicate the five fields and open circles represent the positions of the observed galaxies. The red cross shows the center of the cluster. The Digitized Sky Survey image is shown in the background.
Paper: A Deep Redshift Survey of the Perseus Cluster (A426): Spatial Distribution and Kinematics of Galaxies
Authors: Wooseok Kang, Ho Seong Hwang, Hyunmi Song, Changbom Park, Narae Hwang, Byeong-Gon Park
Figure caption: The size–magnitude diagram of objects in the SDSS photometric catalog. Gray dots represent point sources identified by SDSS, and green triangles are objects originally labeled as extended sources but visually confirmed to be point sources or artifacts. Blue circles and red squares are galaxies without and with spectroscopic redshifts. Only 20% of the data points, excepting the green triangles, are plotted for clarity.
Paper: A Deep Redshift Survey of the Perseus Cluster (A426): Spatial Distribution and Kinematics of Galaxies
Authors: Wooseok Kang, Ho Seong Hwang, Hyunmi Song, Changbom Park, Narae Hwang, Byeong-Gon Park
Figure caption: Color–magnitude diagram of galaxies within 60′ of the cluster center. Gray dots, green squares, red circles, and blue crosses indicate galaxies without spectroscopic redshifts, galaxies with spectroscopic redshifts but interlopers, red members of the cluster, and blue members of the cluster, respectively. The black solid line represents the red sequence calculated by linear fitting. The blue dashed line is 3σ bluer than the red sequence, which is the criterion for classifying blue and red members.
Paper: A Deep Redshift Survey of the Perseus Cluster (A426): Spatial Distribution and Kinematics of Galaxies
Authors: Wooseok Kang, Ho Seong Hwang, Hyunmi Song, Changbom Park, Narae Hwang, Byeong-Gon Park
Figure caption: Comparison of redshifts between sources (left) and within MMT/Hectospec observation in this study (right). On the left panel, z MMT and z Lit denote redshifts measured by MMT/Hectospec and those from the literature, respectively. On the right panel, z 2 is a redshift with a higher r TD than z 1.
Paper: A Deep Redshift Survey of the Perseus Cluster (A426): Spatial Distribution and Kinematics of Galaxies
Authors: Wooseok Kang, Ho Seong Hwang, Hyunmi Song, Changbom Park, Narae Hwang, Byeong-Gon Park
Figure caption: The caustics are plotted in the middle-left panel, along with red member galaxies (red circles), blue member galaxies (blue crosses), and nonmember galaxies (black open circles). The bottom-left panel shows the distribution of apparent magnitude as a function of clustercentric distance. Histograms of galaxies in the field of the Perseus cluster are represented with respect to clustercentric distance (top left), radial velocity (middle right), and apparent magnitude (bottom right). Red and blue histograms are for red and blue members, respectively. Black histograms are for all member galaxies, while open histograms are for all observed galaxies.