adsabs/AstroCLIMB huggingface dataset:
Paper: Tuning the Legacy Survey of Space and Time Observing Strategy for Solar System Science: Incremental Templates in Year 1
Authors: James E. Robinson, Megan E. Schwamb, R. Lynne Jones, Mario Jurić, Peter Yoachim, Bryce T. Bolin, Colin O. Chandler, Steven R. Chesley, Grigori Fedorets, Wesley C. Fraser, Sarah Greenstreet, Henry H. Hsieh, Lauren J. McGinley, Stephanie R. Merritt, Cyrielle Opitom, John K. Parejko
Figure caption: Sky maps showing the total number of visits with templates, per healpixel (nside = 256), at the end of Year 1, assuming a template generation timescale of 7 days. Each panel shows the results for the ugrizy filters. Compare to Figure 2 for the baseline number of visits per pixel at the end of Year 1.
Paper: Tuning the Legacy Survey of Space and Time Observing Strategy for Solar System Science: Incremental Templates in Year 1
Authors: James E. Robinson, Megan E. Schwamb, R. Lynne Jones, Mario Jurić, Peter Yoachim, Bryce T. Bolin, Colin O. Chandler, Steven R. Chesley, Grigori Fedorets, Wesley C. Fraser, Sarah Greenstreet, Henry H. Hsieh, Lauren J. McGinley, Stephanie R. Merritt, Cyrielle Opitom, John K. Parejko
Figure caption: Same as Figure 6 for a template generation timescale of 28 days. Compare to Figure 2 for the baseline number of visits per pixel at the end of Year 1.
Paper: Tuning the Legacy Survey of Space and Time Observing Strategy for Solar System Science: Incremental Templates in Year 1
Authors: James E. Robinson, Megan E. Schwamb, R. Lynne Jones, Mario Jurić, Peter Yoachim, Bryce T. Bolin, Colin O. Chandler, Steven R. Chesley, Grigori Fedorets, Wesley C. Fraser, Sarah Greenstreet, Henry H. Hsieh, Lauren J. McGinley, Stephanie R. Merritt, Cyrielle Opitom, John K. Parejko
Figure caption: Histograms showing the fractional sky area (y-axis) for which healpixels have a given number of visits (x-axis) at the end of Year 1 considering all filters. We show the histogram distribution for the baseline where template generation is assumed (blue). The distributions when template generation is required (Δt = 3, 7, 14, 28 days) are denoted by the colored lines.
Paper: Tuning the Legacy Survey of Space and Time Observing Strategy for Solar System Science: Incremental Templates in Year 1
Authors: James E. Robinson, Megan E. Schwamb, R. Lynne Jones, Mario Jurić, Peter Yoachim, Bryce T. Bolin, Colin O. Chandler, Steven R. Chesley, Grigori Fedorets, Wesley C. Fraser, Sarah Greenstreet, Henry H. Hsieh, Lauren J. McGinley, Stephanie R. Merritt, Cyrielle Opitom, John K. Parejko
Figure caption: The cumulative healpixel area with templates for a given filter in Year 1 of LSST. Each panel shows the results for the ugrizy filters, respectively. The area covered by the nominal one_snap_v4.0 survey (assuming all templates exist at all times) is shown by the black line. The colored lines show the increase in area when template generation is considered (Δt = 3, 7, 14, 28 days). The legend states the total area covered in that filter for the Year 1 baseline survey, and the fraction of that area reached for the different template timescales by the end of Year 1. The cumulative area is approximately related to the cumulative number of visits with templates given that each visit has an area of 9.6 deg2, but that visit may not have 100% template coverage.
Paper: Tuning the Legacy Survey of Space and Time Observing Strategy for Solar System Science: Incremental Templates in Year 1
Authors: James E. Robinson, Megan E. Schwamb, R. Lynne Jones, Mario Jurić, Peter Yoachim, Bryce T. Bolin, Colin O. Chandler, Steven R. Chesley, Grigori Fedorets, Wesley C. Fraser, Sarah Greenstreet, Henry H. Hsieh, Lauren J. McGinley, Stephanie R. Merritt, Cyrielle Opitom, John K. Parejko
Figure caption: Here, we show the results of running the MAF SSO discovery metrics on a redacted Year 1 visit database of visits with template coverage ≥90%. This metric requires three detection pairs over the space of 15 nights. We consider several dynamical populations, which are indicated by color and marker shape. The results of this metric are shown for Δt = 3, 7, 14, 28 days and are given relative to the results for the one_snap_v4.0 Year 1 survey in which templates are assumed to already exist. The left panel shows the discovery metric for the “bright” objects in each population (low H), and the right panel shows the “faint” objects (high H) as defined in Table 3.
Paper: Tuning the Legacy Survey of Space and Time Observing Strategy for Solar System Science: Incremental Templates in Year 1
Authors: James E. Robinson, Megan E. Schwamb, R. Lynne Jones, Mario Jurić, Peter Yoachim, Bryce T. Bolin, Colin O. Chandler, Steven R. Chesley, Grigori Fedorets, Wesley C. Fraser, Sarah Greenstreet, Henry H. Hsieh, Lauren J. McGinley, Stephanie R. Merritt, Cyrielle Opitom, John K. Parejko
Figure caption: Histogram of the fractional template coverage for all Year 1 visits, which is determined from the number of healpixels within the visit footprint with templates. Results are shown for the one_snap_v4.0 cadence simulation, assuming a range of template generation timescales. The peak at zero consists mainly of the images used to make the templates (see Table 5).
Paper: Tuning the Legacy Survey of Space and Time Observing Strategy for Solar System Science: Incremental Templates in Year 1
Authors: James E. Robinson, Megan E. Schwamb, R. Lynne Jones, Mario Jurić, Peter Yoachim, Bryce T. Bolin, Colin O. Chandler, Steven R. Chesley, Grigori Fedorets, Wesley C. Fraser, Sarah Greenstreet, Henry H. Hsieh, Lauren J. McGinley, Stephanie R. Merritt, Cyrielle Opitom, John K. Parejko
Figure caption: Plots showing the discovery completeness of each population during Year 1 of the survey, i.e., the cumulative fraction of objects that have been discovered as a function of time. The line colors distinguish the one_snap_v4.0 survey where template generation has been implicitly assumed and the simulations in which template generation has been considered (for Δt = 3, 7, 14, 28 days). The various panels display results for the different bright/faint dynamical populations considered in this work (see Table 3).
Paper: Tuning the Legacy Survey of Space and Time Observing Strategy for Solar System Science: Incremental Templates in Year 1
Authors: James E. Robinson, Megan E. Schwamb, R. Lynne Jones, Mario Jurić, Peter Yoachim, Bryce T. Bolin, Colin O. Chandler, Steven R. Chesley, Grigori Fedorets, Wesley C. Fraser, Sarah Greenstreet, Henry H. Hsieh, Lauren J. McGinley, Stephanie R. Merritt, Cyrielle Opitom, John K. Parejko
Figure caption: Plots showing the cumulative fraction of the Year 1 survey footprint area (i.e., the unique on-sky area covered by that filter in the Year 1 survey) against deltaNight, which is the number of days between the first visit to a healpixel and the night at which a template was generated. Each panel shows the results for a different filter (ugrizy). The x-axis is limited to show a maximum deltaNight = 90 days to highlight the timescale over which there is the greatest change.
Paper: Tuning the Legacy Survey of Space and Time Observing Strategy for Solar System Science: Incremental Templates in Year 1
Authors: James E. Robinson, Megan E. Schwamb, R. Lynne Jones, Mario Jurić, Peter Yoachim, Bryce T. Bolin, Colin O. Chandler, Steven R. Chesley, Grigori Fedorets, Wesley C. Fraser, Sarah Greenstreet, Henry H. Hsieh, Lauren J. McGinley, Stephanie R. Merritt, Cyrielle Opitom, John K. Parejko
Figure caption: Two-dimensional histogram distributions showing how the fractional template coverage of all visits changes as a function of survey time. The logarithmic color scale indicates the number of visits with a particular template coverage on a given date. Results are shown for Δt = 7 days (left) and Δt = 28 days (right). Empty spaces indicate survey downtime when no visits were taken, e.g., scheduled maintenance around night 150.
Paper: Tuning the Legacy Survey of Space and Time Observing Strategy for Solar System Science: Incremental Templates in Year 1
Authors: James E. Robinson, Megan E. Schwamb, R. Lynne Jones, Mario Jurić, Peter Yoachim, Bryce T. Bolin, Colin O. Chandler, Steven R. Chesley, Grigori Fedorets, Wesley C. Fraser, Sarah Greenstreet, Henry H. Hsieh, Lauren J. McGinley, Stephanie R. Merritt, Cyrielle Opitom, John K. Parejko
Figure caption: An alternative to Figure 15, where now we plot the cumulative area covered in a particular filter as a fraction of the total baseline area in that filter at the end of Year 1. In each panel, we show the results for Δt = 3, 7, 14, 28 days, and the color of each curve indicates the filter (ugrizy). The legend provides the fractional cumulative area compared to the baseline at the end of Year 1 for each filter.
Paper: Tuning the Legacy Survey of Space and Time Observing Strategy for Solar System Science: Incremental Templates in Year 1
Authors: James E. Robinson, Megan E. Schwamb, R. Lynne Jones, Mario Jurić, Peter Yoachim, Bryce T. Bolin, Colin O. Chandler, Steven R. Chesley, Grigori Fedorets, Wesley C. Fraser, Sarah Greenstreet, Henry H. Hsieh, Lauren J. McGinley, Stephanie R. Merritt, Cyrielle Opitom, John K. Parejko
Figure caption: These panels show the same discovery metrics as Figure 21, but now, results are for subsets of visits with decl. < 0∘ and decl. ≥ 0∘ (upper and lower panels, respectively).
Paper: Infrared-selected Active Galactic Nuclei in the Kepler Fields
Authors: Tran Tsan, Matthew Malkan, Rick Edelson, Krista Smith, Daniel Stern, Matthew Graham
Figure caption: Sky map of Lick, Palomar, and Keck candidates and the corresponding Kepler campaigns. The gray panels mark the positions of the CCDs during the campaigns: K2 (K2 footprints: https://github.com/KeplerGO/K2FootprintFiles); (c#) and the Kepler Prime field (K prime footprint: https://archive.stsci.edu/missions-and-data/kepler/field). The solid circles indicate objects observed by Kepler and have light-curve (LC) data, while empty circles mean they do not have any LC data. These objects were either slightly outside the Kepler campaign fields, between CCD slots, or part of bad CCD slots. Thus, they did not have any data.
Paper: Infrared-selected Active Galactic Nuclei in the Kepler Fields
Authors: Tran Tsan, Matthew Malkan, Rick Edelson, Krista Smith, Daniel Stern, Matthew Graham
Figure caption: Continuum flux. We plotted the continuum flux at 7000 Å, Fν (erg s−1 cm−2 Hz−1), against their r-band flux for 47 objects. The black line is the best-fit line, while the green-dashed line is our estimate of the decrement Fν/Fr−band = 1/2. The RMSEs for both lines are 0.45.
Paper: Infrared-selected Active Galactic Nuclei in the Kepler Fields
Authors: Tran Tsan, Matthew Malkan, Rick Edelson, Krista Smith, Daniel Stern, Matthew Graham
Figure caption: Deblending of Hα + [N II]. The two [N II] 6548 Å and 6584 Å have FWHMs of 703 and 393 km s−1, respectively. As the fit (dotted line) illustrates, splot tried to fit narrow [N II]-6548; however, it mistook the wing of Hα as part of [N II]. Hence, the fit overestimated the actual width. The broad and narrow components of Hα have FWHMs of 2318 and 329 km s−1, respectively.
Paper: A Deep Learning Method for Automatic Coronal Mass Ejection Feature Identification
Authors: P. Yang, H. S. Fu, J. B. Cao, F. Shen
Figure caption: CME segmentation network (TransCME). The left half of the TransCME diagram, represented by the dashed box on the left, shows the encoder structure, while the right half, represented by the dashed box on the right, shows the decoder structure. The interpretation of CBAM–ASPP at the bottom shows the structure of the CBAM and ASPP parts in the decoder.
Paper: A Deep Learning Method for Automatic Coronal Mass Ejection Feature Identification
Authors: P. Yang, H. S. Fu, J. B. Cao, F. Shen
Figure caption: CME tracking flowchart. After detecting the CME structure, IoU, Kalman filter, and Hungarian algorithm are used to quickly track multiple identified CMEs. Max_age is the maximum number of frames lost in a single tracking of CME, while min_hits is the minimum number of frames successfully tracked for CME targets.
Paper: A Deep Learning Method for Automatic Coronal Mass Ejection Feature Identification
Authors: P. Yang, H. S. Fu, J. B. Cao, F. Shen
Figure caption: Tracking results and height–time fitting. The first two rows of images are time-series CME event tracking. ID 7120 was detected in the first frame, reached its highest point in the fifth frame, and continued to track until it disappeared. In the fourth frame, a new CME event appears and ID 7121 is detected until it disappears in the seventh frame. The bottom row of images shows the height over time fitting of these two CME events.
Paper: A Deep Learning Method for Automatic Coronal Mass Ejection Feature Identification
Authors: P. Yang, H. S. Fu, J. B. Cao, F. Shen
Figure caption: CME segmentation results. The first row represents some of the image frames we have selected, while the second, third, and fourth rows represent DNANet, SegFormer, and the detection results of our method, respectively.
Paper: Infrared-selected Active Galactic Nuclei in the Kepler Fields
Authors: Tran Tsan, Matthew Malkan, Rick Edelson, Krista Smith, Daniel Stern, Matthew Graham
Figure caption: Flux measurements of [O III] vs. [O II] in logarithm. The result yields a correlation of log [O III] (erg s−1 cm−2) = (0.7 ± 0.1) log [O II] − (4 ± 1), indicating that [O II] rose much faster in flux than [O III].
Paper: A Deep Learning Method for Automatic Coronal Mass Ejection Feature Identification
Authors: P. Yang, H. S. Fu, J. B. Cao, F. Shen
Figure caption: Expansion of the data set for both original and annotated images. The first image in the first row is the original image, and the first image in the third row is the image we annotated. The rest are our data-augmented images.
Paper: A Deep Learning Method for Automatic Coronal Mass Ejection Feature Identification
Authors: P. Yang, H. S. Fu, J. B. Cao, F. Shen
Figure caption: The proposed overall process for automatic identification of CME characteristics.
Paper: Research on High-precision Fiber Transmission and Compensation for a Solar Radio Heliograph
Authors: Liye Zhu, 力业 朱, Shuqi Li, 舒琪 李, Yizhang Zhang, 义章 张, Yonglin Yu, 永林 于, Xiaofeng Xu, 晓峰 徐, Qinzheng Li, 钦正 李, Zilei Yang, 子磊 杨, Zhao Wu, 昭 武, Yanrui Su, 艳蕊 苏, HongQiang Song, 红强 宋, Yao Chen, 耀 陈, Fabao Yan, 发宝 严
Figure caption: The transmission scheme used in this paper.
Paper: Research on High-precision Fiber Transmission and Compensation for a Solar Radio Heliograph
Authors: Liye Zhu, 力业 朱, Shuqi Li, 舒琪 李, Yizhang Zhang, 义章 张, Yonglin Yu, 永林 于, Xiaofeng Xu, 晓峰 徐, Qinzheng Li, 钦正 李, Zilei Yang, 子磊 杨, Zhao Wu, 昭 武, Yanrui Su, 艳蕊 苏, HongQiang Song, 红强 宋, Yao Chen, 耀 陈, Fabao Yan, 发宝 严
Figure caption: The loop-back method.
Paper: Research on High-precision Fiber Transmission and Compensation for a Solar Radio Heliograph
Authors: Liye Zhu, 力业 朱, Shuqi Li, 舒琪 李, Yizhang Zhang, 义章 张, Yonglin Yu, 永林 于, Xiaofeng Xu, 晓峰 徐, Qinzheng Li, 钦正 李, Zilei Yang, 子磊 杨, Zhao Wu, 昭 武, Yanrui Su, 艳蕊 苏, HongQiang Song, 红强 宋, Yao Chen, 耀 陈, Fabao Yan, 发宝 严
Figure caption: Comparison of frequency values. (a) The 200 m fiber. (b) The 1000 m fiber.
Paper: Research on High-precision Fiber Transmission and Compensation for a Solar Radio Heliograph
Authors: Liye Zhu, 力业 朱, Shuqi Li, 舒琪 李, Yizhang Zhang, 义章 张, Yonglin Yu, 永林 于, Xiaofeng Xu, 晓峰 徐, Qinzheng Li, 钦正 李, Zilei Yang, 子磊 杨, Zhao Wu, 昭 武, Yanrui Su, 艳蕊 苏, HongQiang Song, 红强 宋, Yao Chen, 耀 陈, Fabao Yan, 发宝 严
Figure caption: (a) The frequency standard. (b) The time standard.
Paper: Research on High-precision Fiber Transmission and Compensation for a Solar Radio Heliograph
Authors: Liye Zhu, 力业 朱, Shuqi Li, 舒琪 李, Yizhang Zhang, 义章 张, Yonglin Yu, 永林 于, Xiaofeng Xu, 晓峰 徐, Qinzheng Li, 钦正 李, Zilei Yang, 子磊 杨, Zhao Wu, 昭 武, Yanrui Su, 艳蕊 苏, HongQiang Song, 红强 宋, Yao Chen, 耀 陈, Fabao Yan, 发宝 严
Figure caption: (a) T-array heliograph. (b) Fiber transmission links.
Paper: Research on High-precision Fiber Transmission and Compensation for a Solar Radio Heliograph
Authors: Liye Zhu, 力业 朱, Shuqi Li, 舒琪 李, Yizhang Zhang, 义章 张, Yonglin Yu, 永林 于, Xiaofeng Xu, 晓峰 徐, Qinzheng Li, 钦正 李, Zilei Yang, 子磊 杨, Zhao Wu, 昭 武, Yanrui Su, 艳蕊 苏, HongQiang Song, 红强 宋, Yao Chen, 耀 陈, Fabao Yan, 发宝 严
Figure caption: Diagram of the two-link frequency standard synchronous transmission system.
Paper: Research on High-precision Fiber Transmission and Compensation for a Solar Radio Heliograph
Authors: Liye Zhu, 力业 朱, Shuqi Li, 舒琪 李, Yizhang Zhang, 义章 张, Yonglin Yu, 永林 于, Xiaofeng Xu, 晓峰 徐, Qinzheng Li, 钦正 李, Zilei Yang, 子磊 杨, Zhao Wu, 昭 武, Yanrui Su, 艳蕊 苏, HongQiang Song, 红强 宋, Yao Chen, 耀 陈, Fabao Yan, 发宝 严
Figure caption: The link setup and testing.
Paper: Research on High-precision Fiber Transmission and Compensation for a Solar Radio Heliograph
Authors: Liye Zhu, 力业 朱, Shuqi Li, 舒琪 李, Yizhang Zhang, 义章 张, Yonglin Yu, 永林 于, Xiaofeng Xu, 晓峰 徐, Qinzheng Li, 钦正 李, Zilei Yang, 子磊 杨, Zhao Wu, 昭 武, Yanrui Su, 艳蕊 苏, HongQiang Song, 红强 宋, Yao Chen, 耀 陈, Fabao Yan, 发宝 严
Figure caption: The application in distributed synchronous acquisition.
Paper: Mock Observations: Morphological Analysis of Galaxies in TNG100 Simulations
Authors: Jun-Yu Gong, Weipeng Lin, Lin Tang, Yanyao Lan
Figure caption: The relationship between various galaxy morphological parameters and stellar mass is shown for different galaxy samples: all galaxies, ETG, LTG, central galaxies, and satellite galaxies. From top to bottom, the rows represent C, A, Gini, M20, n, and Re. In each panel, data points indicate mock observation (MO) results, with colors representing the g − r color (redder points for higher g − r, bluer points for lower g − r). Black points show the median values within each stellar mass bin, with error bars denoting the standard deviation. The black solid line represents the SDSS results, with the gray shaded area illustrating the standard deviation as error.
Paper: Mock Observations: Morphological Analysis of Galaxies in TNG100 Simulations
Authors: Jun-Yu Gong, Weipeng Lin, Lin Tang, Yanyao Lan
Figure caption: Normalized histograms illustrating the comparison between mock observation (labeled as “MO” in the figure) and SDSS data for various galaxy morphological parameters. Each subplot represents a different parameter: C, A, Gini, M20, n, and Re, arranged from left to right in columns. Solid lines represent mock observation data, while dashed–dotted lines represent SDSS data. Dotted lines indicate the median values, with error denoting the standard deviation. Red lines denote ETGs, blue lines represent LTGs, dark orange lines represent central galaxies (labeled as “Cen” in the figure), and dark purple lines represent satellite galaxies (labeled as “Sat” in the figure). The figure also shows four specific morphological types: elliptical galaxies (labeled as “Ell” in the figure) with deep red lines, S0/a galaxies with orange lines, Sab/Sb galaxies with green lines, and Sc/Sd/Irr galaxies with deep pink lines. All histograms are normalized to a maximum value of 1.
Paper: Mock Observations: Morphological Analysis of Galaxies in TNG100 Simulations
Authors: Jun-Yu Gong, Weipeng Lin, Lin Tang, Yanyao Lan
Figure caption: Visualization of the impact of simulated observational effects on galaxy classification via a confusion matrix. This matrix compares the morphological classifications of the same galaxies obtained using Model 1 (applied to the mock observation data) and Model 4 (applied to the original TNG100 data without observational effects). Each cell shows the number count and percentage of galaxies falling into that specific classification combination: red indicates galaxies classified as ETGs by both models; green indicates classification as ETG by Model 1 but LTG by Model 4; yellow indicates LTG by Model 1 but ETG by Model 4; and blue indicates galaxies classified as LTGs by both models.
Paper: Mock Observations: Morphological Analysis of Galaxies in TNG100 Simulations
Authors: Jun-Yu Gong, Weipeng Lin, Lin Tang, Yanyao Lan
Figure caption: The same as Figure 3, but for the TNG100 subsamples, with “TNG100” in the legend in contrast to “MO” used for mock observation in Figure 3.
Paper: Mock Observations: Morphological Analysis of Galaxies in TNG100 Simulations
Authors: Jun-Yu Gong, Weipeng Lin, Lin Tang, Yanyao Lan
Figure caption: Illustration of SDSS observations, TNG100 data, and the resultant of mock observations. The top row displays actual SDSS observational data for selected galaxies, with object IDs (SDSS J2000 object identifier) provided. The middle row exhibits the TNG100 data galaxies in the x–y projected plane for z = 0.01. The bottom row presents the TNG100 data after undergoing our mock observation processing, which includes PSF convolution, background addition, and noise simulation. The left two columns are ETGs, while the right two columns are LTGs. The scale in the bottom left corner indicates the 10″ scale.
Paper: Mock Observations: Morphological Analysis of Galaxies in TNG100 Simulations
Authors: Jun-Yu Gong, Weipeng Lin, Lin Tang, Yanyao Lan
Figure caption: Stellar mass histograms for mock observation data (left column) and SDSS data (right column). The top row shows ETGs (in red) and LTGs (in blue), and the combined sample of all galaxies (black). The bottom row is separated into morphological subsamples, including ellipticals (dark red), S0/Sa (orange), Sab/Sb (green), and Sc/Sd/Irr (deep pink). Each data is normalized such that the integral area equals 1.
Paper: Mock Observations: Morphological Analysis of Galaxies in TNG100 Simulations
Authors: Jun-Yu Gong, Weipeng Lin, Lin Tang, Yanyao Lan
Figure caption: The same as Figure 3, but showing the relationship between various galaxy morphological parameters and stellar mass for different galaxy type: all galaxies, elliptical galaxies, S0/a galaxies, Sab/Sb galaxies, and Sc/Sd/Irr galaxies.
Paper: Mock Observations: Morphological Analysis of Galaxies in TNG100 Simulations
Authors: Jun-Yu Gong, Weipeng Lin, Lin Tang, Yanyao Lan
Figure caption: Histograms of the Sérsic index derived from various data sets, in comparison to those from SDSS. The top left panel showcases mock observation result from this study, and the results from H19 in the top right panel. The bottom panel elucidates findings from mock observations applied to TNG (bottom left) and Illustris (bottom right) synthetic image data according to R19, adopting the methodology delineated in our research. Across all panels, ETGs are represented in red, and LTGs in blue. Black dashed and dotted lines correspond to SDSS ETGs and LTGs, respectively. Median values along with their standard deviations, indicating error, are annotated in the legend.
Paper: Mock Observations: Morphological Analysis of Galaxies in TNG100 Simulations
Authors: Jun-Yu Gong, Weipeng Lin, Lin Tang, Yanyao Lan
Figure caption: The same as Figure 3, but for the Gaussian mock observation subsamples, where “GMO” in the legend represents Gaussian mock observation, in contrast to “MO” used for mock observation in Figure 3.
Paper: Mock Observations: Morphological Analysis of Galaxies in TNG100 Simulations
Authors: Jun-Yu Gong, Weipeng Lin, Lin Tang, Yanyao Lan
Figure caption: Histograms of the C coefficient derived from various data sets, in comparison to those from SDSS. The format is the same as Figure 9.
Paper: Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data
Authors: Zhijian Luo, Jianzhen Chen, Zhu Chen, Shaohua Zhang, Liping Fu, Hubing Xiao, Chenggang Shu
Figure caption: The network architecture of GC-SWGAN. The symbol “z” denotes the input random noise vector, “Xunlabeled” represents the input unlabeled real images, “Xlabeled” indicates the input labeled real images, and “Xfake” refers to the fake images generated by the generator. G is the generator, which employs a multilayer convolutional network. D is the discriminator, whose task is to distinguish between real and fake images. C represents the classifier, which is responsible for learning to assign the correct class labels to real samples.