adsabs/AstroCLIMB huggingface dataset:
Paper:  cogsworth: A Gala of COSMIC Proportions Combining Binary Stellar Evolution and Galactic Dynamics
Authors: Tom Wagg, Katelyn Breivik, Mathieu Renzo, Adrian M. Price-Whelan
Figure caption: The relationship between galactocentric radius, metallicity, and lookback time in the Wagg2022 SFH. Left: the main panel shows a 2D histogram of the number of stars formed at a given radius and metallicity, with marginal distributions for each parameter shown as histograms on the side panels. Right: the main panel shows a 2D histogram in which each bin is colored by the average lookback time of associated stars. Marginal distributions are now shown as stacked histograms, grouped by lookback time.✎
Paper:  cogsworth: A Gala of COSMIC Proportions Combining Binary Stellar Evolution and Galactic Dynamics
Authors: Tom Wagg, Katelyn Breivik, Mathieu Renzo, Adrian M. Price-Whelan
Figure caption: The scaling of cogsworth’s runtime with the number of processes used for a fixed population size of 10,000 binaries. The black points indicate the total runtime, while the shaded areas show the relative contribution from the stellar evolution and galactic orbit integration. The right y-axis and gray dashed line shows the relative speedup compared to the simulation using a single process.✎
Paper:  cogsworth: A Gala of COSMIC Proportions Combining Binary Stellar Evolution and Galactic Dynamics
Authors: Tom Wagg, Katelyn Breivik, Mathieu Renzo, Adrian M. Price-Whelan
Figure caption: A dynamically generated cartoon binary evolution timeline. Each row shows an evolutionary step, labeled with its time and the event that occurred. Circles are shown for each star, annotated with their masses and the binary’s orbital period. A dashed line indicates a bound binary, while a lack thereof indicates two unbound stars that were previously in a binary. ✎
Paper:  cogsworth: A Gala of COSMIC Proportions Combining Binary Stellar Evolution and Galactic Dynamics
Authors: Tom Wagg, Katelyn Breivik, Mathieu Renzo, Adrian M. Price-Whelan
Figure caption: Binary evolution and the presence of a galactic potential both have a significant impact on the spatial distribution of stars. Each panel shows the spatial distributions of massive stars at present day from a cogsworth population of single stars (left), binaries (right, top) and binaries evolved without a galactic potential (right, bottom). Density distributions are computed using kernel density estimators (KDEs) and shown (in linearly spaced iso-levels) up to the 98th percentiles, with the remaining stars plotted as scatter points. Marginal KDEs are shown on the right with a log scale.✎
Paper:  cogsworth: A Gala of COSMIC Proportions Combining Binary Stellar Evolution and Galactic Dynamics
Authors: Tom Wagg, Katelyn Breivik, Mathieu Renzo, Adrian M. Price-Whelan
Figure caption: Similar to Figure 4, but comparing the impact of varying binary physics and the galactic potential. The axes are now in units of scale radius and scale height for better comparison between the two galaxies. Each quadrant corresponds to a different combination of common-envelope efficiency and galactic potential (annotated in each).✎
Paper:  cogsworth: A Gala of COSMIC Proportions Combining Binary Stellar Evolution and Galactic Dynamics
Authors: Tom Wagg, Katelyn Breivik, Mathieu Renzo, Adrian M. Price-Whelan
Figure caption: An example star cluster, sampled from a single star particle in the FIRE m11h hydrodynamical zoom-in simulation, evolved within a cogsworth simulation. Each line shows the orbit of a star in the cluster. Colored lines are for stars that had supernova events (highlighted by scatter points), while the rest are shown in gray. For binaries that disrupt, an additional dashed line is shown for the subsequent orbit of the ejected companion.✎
Paper:  cogsworth: A Gala of COSMIC Proportions Combining Binary Stellar Evolution and Galactic Dynamics
Authors: Tom Wagg, Katelyn Breivik, Mathieu Renzo, Adrian M. Price-Whelan
Figure caption:  cogsworth combines population synthesis and galactic dynamics self-consistently to produce rates, distributions, observables, and kinematics of massive binaries and compact objects. A schematic of the input options and outputs of cogsworth simulations.
Paper:  cogsworth: A Gala of COSMIC Proportions Combining Binary Stellar Evolution and Galactic Dynamics
Authors: Tom Wagg, Katelyn Breivik, Mathieu Renzo, Adrian M. Price-Whelan
Figure caption: Present-day sky positions of disrupted binaries, with and without supernova kicks. A square (plus) marker shows the location of the primary (secondary) star from a disrupted binary, connected to its companion by a dashed line. Circular markers indicate the location of the binary had no supernova kicks occurred. The background density shows a 2D histogram of 500,000 stars sampled from the same SFH (Wagg2022) for comparison. Note that disk appears extended in latitude because we limit the axes to the relevant region.✎
Paper:  cogsworth: A Gala of COSMIC Proportions Combining Binary Stellar Evolution and Galactic Dynamics
Authors: Tom Wagg, Katelyn Breivik, Mathieu Renzo, Adrian M. Price-Whelan
Figure caption: An example simulated Gaia CMD. Each scatter point corresponds to a binary or disrupted star, colored by its stellar type (using the stellar type of the brighter component for binaries). The stellar types abbreviated in the color bar follow those defined in BSE (see Section 4 of J. R. Hurley et al. 2000).✎
Paper: A Study of Real-time Detection Methods for Solar Radio Burst Identification
Authors: YongLin Yu, 永林 于, ChengLong Qiao, 成龙 乔, Shuo Xu, 硕 许, QinZheng Li, 钦正 李, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, FaBao Yan, 发宝 严
Figure caption: Detection results at different stages of the automatic detection system for solar radio bursts based on adaptive CFAR (H. Salmane et al. 2018). (a) Original image; (b) image after filtering; (c) binary image; (d) image after applying the Radon transform.
Paper: A Study of Real-time Detection Methods for Solar Radio Burst Identification
Authors: YongLin Yu, 永林 于, ChengLong Qiao, 成龙 乔, Shuo Xu, 硕 许, QinZheng Li, 钦正 李, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, FaBao Yan, 发宝 严
Figure caption: Comparison of the five types of solar radio bursts.
Paper: A Study of Real-time Detection Methods for Solar Radio Burst Identification
Authors: YongLin Yu, 永林 于, ChengLong Qiao, 成龙 乔, Shuo Xu, 硕 许, QinZheng Li, 钦正 李, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, FaBao Yan, 发宝 严
Figure caption: Data from the Nobeyama Radio Observatory (source: official website).
Paper: A Study of Real-time Detection Methods for Solar Radio Burst Identification
Authors: YongLin Yu, 永林 于, ChengLong Qiao, 成龙 乔, Shuo Xu, 硕 许, QinZheng Li, 钦正 李, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, FaBao Yan, 发宝 严
Figure caption: Solar burst event observed by NoRP on 2015 March 10.
Paper: A Study of Real-time Detection Methods for Solar Radio Burst Identification
Authors: YongLin Yu, 永林 于, ChengLong Qiao, 成龙 乔, Shuo Xu, 硕 许, QinZheng Li, 钦正 李, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, FaBao Yan, 发宝 严
Figure caption: Partial detection flowchart of the Continuous Slope method.
Paper: A Study of Real-time Detection Methods for Solar Radio Burst Identification
Authors: YongLin Yu, 永林 于, ChengLong Qiao, 成龙 乔, Shuo Xu, 硕 许, QinZheng Li, 钦正 李, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, FaBao Yan, 发宝 严
Figure caption: Flowchart of the Continuous-3σ method.
Paper: A Study of Real-time Detection Methods for Solar Radio Burst Identification
Authors: YongLin Yu, 永林 于, ChengLong Qiao, 成龙 乔, Shuo Xu, 硕 许, QinZheng Li, 钦正 李, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, FaBao Yan, 发宝 严
Figure caption: Partial detection flowchart of the Sum Flux-3σ method.
Paper: A Study of Real-time Detection Methods for Solar Radio Burst Identification
Authors: YongLin Yu, 永林 于, ChengLong Qiao, 成龙 乔, Shuo Xu, 硕 许, QinZheng Li, 钦正 李, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, FaBao Yan, 发宝 严
Figure caption: The experimental results comparison chart of different 3σ methods.
Paper: A Study of Real-time Detection Methods for Solar Radio Burst Identification
Authors: YongLin Yu, 永林 于, ChengLong Qiao, 成龙 乔, Shuo Xu, 硕 许, QinZheng Li, 钦正 李, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, FaBao Yan, 发宝 严
Figure caption: Comparison of success rates and false alarm counts for different methods.
Paper: A Study of Real-time Detection Methods for Solar Radio Burst Identification
Authors: YongLin Yu, 永林 于, ChengLong Qiao, 成龙 乔, Shuo Xu, 硕 许, QinZheng Li, 钦正 李, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, FaBao Yan, 发宝 严
Figure caption: Upper computer solar bursts detection page.
Paper: A Study of Real-time Detection Methods for Solar Radio Burst Identification
Authors: YongLin Yu, 永林 于, ChengLong Qiao, 成龙 乔, Shuo Xu, 硕 许, QinZheng Li, 钦正 李, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, FaBao Yan, 发宝 严
Figure caption: Partial detection flowchart of the Sum Flux Continuous-3σ method.
Paper: A Study of Real-time Detection Methods for Solar Radio Burst Identification
Authors: YongLin Yu, 永林 于, ChengLong Qiao, 成龙 乔, Shuo Xu, 硕 许, QinZheng Li, 钦正 李, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, FaBao Yan, 发宝 严
Figure caption: Burst detection page of the upper computer at the end of solar radio bursts.
Paper: A Study of Real-time Detection Methods for Solar Radio Burst Identification
Authors: YongLin Yu, 永林 于, ChengLong Qiao, 成龙 乔, Shuo Xu, 硕 许, QinZheng Li, 钦正 李, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, FaBao Yan, 发宝 严
Figure caption: Burst detection page of the upper computer at the onset of solar radio bursts.
Paper: A Study of Real-time Detection Methods for Solar Radio Burst Identification
Authors: YongLin Yu, 永林 于, ChengLong Qiao, 成龙 乔, Shuo Xu, 硕 许, QinZheng Li, 钦正 李, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, FaBao Yan, 发宝 严
Figure caption: Partial detection flowchart of the Sum Flux Slope method.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: The spectroscopic redshift, r-band magnitude, and richness distribution of samples in the training set (dashed lines) and the test set (solid lines) used for training the Richness Estimator. The leftmost panels are for BCGs in the positive sample and for normal galaxies in the training data.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: An example of an SOM (right panel) of a WHL cluster with its corresponding color image (left panel). The size of the SOM is 200 pixels × 200 pixels over a physical area of 2 × 2 Mpc2 and the physical scale of the color image is the same as that of the SOM.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: The ROC curves of the training set and test set. The AUC value is the area between the curve and the axis. The green dashed–dotted line represents an AUC value of 0.5, which means the model has no ability of classification.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: The redshift and r-band magnitude distribution of galaxies of PhotCat in the training set (dashed lines) and test set (solid lines).
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: The richness distribution of WHL clusters with spectroscopic redshifts. The colorbar represents the r-band magnitude of their BCGs. Only 20% of the WHL clusters are randomly selected for plotting.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: Full workflow. The COSMIC algorithm consists of two main steps, BCG classification and richness estimation.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: The full architecture of our model. The feature extractor represents the ResNet-34 pretrained model and the blocks with numbers represent the FC layers comprising the regression network with the corresponding number of units. The output is the richness of the galaxy cluster.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: Color–magnitude diagram of BCG candidates compared with WHL clusters. The top panel displays the g − i color, while the bottom panel shows the r − i color. The horizontal axis represents the r-band magnitude for both BCG candidates (red dots) and the BCGs of WHL galaxy clusters (black dots), where only 20% of them are randomly selected for plotting.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: The richness distributions of all clusters (dashed line) and new ones (solid line) under three richness bins.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: The richness comparison between our predicted richness and the WHL richness in the training and test sets.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: The redshift and r-band magnitude distributions of the test data.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: The matched fraction across different richness intervals of four SDSS cluster catalogs.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: The richness distributions of all clusters (dashed line) and new ones (solid line).
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: Color images of three newly identified clusters from the DESI Legacy Surveys. The physical radius of the images is equal to their r 500.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: The redshift and r-band magnitude distributions of BCG candidates in the test data.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: The left panel shows the matched (black dots) and unmatched (red crosses) eROSITA clusters in the mass–redshift diagram. The right panel shows the correlations between our estimated richness and the richness (LAMBDA_NORM) in the eROSITA catalog. The circles and dots represent the matched clusters using different matching separations of 2″ and 1.5 r 500, respectively. The dashed line represents y = x.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: The left panel shows the matched (black dots) and unmatched (red crosses) RASS-MCMF clusters in the mass–redshift diagram. The middle and right panels show the correlations of our estimated richness with both M 500 and the richness (Lambda1) in the RASS-MCMF catalog. The circles and dots represent the matched clusters using different matching separations of 2″ and 1.5 r 500, respectively. The dashed line represents y = x.
Paper: COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Authors: Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Figure caption: Richness comparison with optical cluster catalogs. The top two panels show the comparison to cluster richness in the redMaPPer and Zou+21 catalogs. The bottom two panels show the comparison to the group mass in the Yang+21 catalog and the log stellar mass of the cluster within a 1 Mpc physical radius, which is corrected for incompleteness and galaxy background, in the CluMPR catalog. The dashed line represents y = x.
Paper: Investigations of MWISP Filaments. I. Filament Identification and Analysis Algorithms, and Source Catalog
Authors: Yu Jiang, Xuepeng Chen, Sheng Zheng, Zhibo Jiang, Zhiwei Chen, Yao Huang, Yang Su, Li Sun, Jian-Cheng Feng, Haoran Feng, Ji Yang
Figure caption: An example diagram illustrating the identification process. The background features a velocity-integrated intensity map of a filament, as identified in Figure 1. Red circles indicate the positions of the clumps, numbered accordingly, while red lines represent the direction of each clump’s principal axis. Clumps 1 and 2 are neighboring clumps, with the cyan line connecting their positions. The blue curve shows the angle between the direction of clump 2 and the cyan line. Notably, clumps 1 and 2 demonstrate directional consistency with the local filament axis represented by the cyan line. Clumps 2 and 3, as well as clumps 3 and 5, are also neighboring pairs. The green lines are perpendicular to the cyan line for clumps 3 and 5. It can be observed that clumps 3 and 5 have positional consistency with the local filament axis.
Paper: Investigations of MWISP Filaments. I. Filament Identification and Analysis Algorithms, and Source Catalog
Authors: Yu Jiang, Xuepeng Chen, Sheng Zheng, Zhibo Jiang, Zhiwei Chen, Yao Huang, Yang Su, Li Sun, Jian-Cheng Feng, Haoran Feng, Ji Yang
Figure caption: The direction and position of clumps. The total number of clumps is 126, with 88 of them not touching the edge. The asterisks denote the spatial positions of the clumps, and the different colors of the asterisks denote different velocity positions. The red lines illustrate the direction of the principal axis of the clumps at the untouched edges.
Paper: Investigations of MWISP Filaments. I. Filament Identification and Analysis Algorithms, and Source Catalog
Authors: Yu Jiang, Xuepeng Chen, Sheng Zheng, Zhibo Jiang, Zhiwei Chen, Yao Huang, Yang Su, Li Sun, Jian-Cheng Feng, Haoran Feng, Ji Yang
Figure caption: Integrated intensity maps of a filament in different directions. Left panel: velocity-integrated intensity map. The central position of the filament, denoted by a red asterisk, has coordinates (l, b, v) = (18﹩\mathop{.}\limits^{\circ }﹩11, 0﹩\mathop{.}\limits^{\circ }﹩41, 22.91 km s−1), and its principal axis derived through principal component analysis is represented by a green line with an angle of 86﹩\mathop{.}\limits^{\circ }﹩16. The yellow contours delineate the boundaries of the filament region. Middle panel: latitude-integrated intensity map. Right panel: longitude-integrated intensity map.
Paper: Investigations of MWISP Filaments. I. Filament Identification and Analysis Algorithms, and Source Catalog
Authors: Yu Jiang, Xuepeng Chen, Sheng Zheng, Zhibo Jiang, Zhiwei Chen, Yao Huang, Yang Su, Li Sun, Jian-Cheng Feng, Haoran Feng, Ji Yang
Figure caption: An example data to illustrate DPConCFil. The data are the 13CO emission of MWISP within 17﹩\mathop{.}\limits^{\circ }﹩7 ≤ l ≤ 18﹩\mathop{.}\limits^{\circ }﹩5, 0° ≤ b ≤ 0﹩\mathop{.}\limits^{\circ }﹩8, and 5 km s−1 ≤ v ≤ 30 km s−1.
Paper: Investigations of MWISP Filaments. I. Filament Identification and Analysis Algorithms, and Source Catalog
Authors: Yu Jiang, Xuepeng Chen, Sheng Zheng, Zhibo Jiang, Zhiwei Chen, Yao Huang, Yang Su, Li Sun, Jian-Cheng Feng, Haoran Feng, Ji Yang
Figure caption: (a) Intensity profiles of Figure 5(c). The gray lines are the profiles, the red line is the average line of the profiles after filtering out abnormal profiles (see the manual), and the blue line is the reflection of the left average line along the dashed symmetry axis. The golden profile is obtained by fitting the profiles with a Gaussian function, while the purple profile is obtained by fitting the profiles with a Plummer function. The symmetry of the average profile is 0.68. The FWHM obtained from the Gaussian fitting is FWHM G  = 6.27 ± 0.36 pixels, while the FWHM from Plummer-like fitting is FWHM P  = 3.74 ± 1.05 pixels, and its power index is p = 2.2 ± 0.28. (b) Intensity profiles of Figure 7(c). The symmetry of the average profile is 0.76. The FWHM obtained from the Gaussian fitting is FWHM G  = 6.17 ± 0.31 pixels, while the FWHM from Plummer-like fitting is FWHM P  = 4.15 ± 1.07 pixels, and its power index is p = 2.3 ± 0.32.
Paper: Investigations of MWISP Filaments. I. Filament Identification and Analysis Algorithms, and Source Catalog
Authors: Yu Jiang, Xuepeng Chen, Sheng Zheng, Zhibo Jiang, Zhiwei Chen, Yao Huang, Yang Su, Li Sun, Jian-Cheng Feng, Haoran Feng, Ji Yang
Figure caption: Distribution of all filaments in the Galactic longitude–latitude and Galactic longitude–velocity planes in the application data. The background map illustrates the distribution of molecular gas traced by the integrated 13CO emission. Dark blue circles denote filaments identified by DPConCFil, and royal blue plus signs indicate those with a greater number of clumps. Lime green circles represent structures isolated by MST, and dark green crosses highlight filaments with greater linearity. Red asterisks mark giant filaments identified by DPConCFil, and orange stars denote those identified by MST; details are in Appendix D. Different colored dashed lines illustrate various spiral arms from the model by M. J. Reid et al. (2019), with their names labeled.
Paper: Investigations of MWISP Filaments. I. Filament Identification and Analysis Algorithms, and Source Catalog
Authors: Yu Jiang, Xuepeng Chen, Sheng Zheng, Zhibo Jiang, Zhiwei Chen, Yao Huang, Yang Su, Li Sun, Jian-Cheng Feng, Haoran Feng, Ji Yang
Figure caption: Intensity skeleton analysis. (a) The initial intensity skeleton extracted using the graph-based skeletonization method; (b) the final thinned intensity skeleton; (c) the fitted intensity skeleton and profiles. The thicker red curve represents the smoothed intensity skeleton obtained through B-spline fitting, while the thinner red straight lines represent the profiles. The blue scatter points denote the peak intensity pixels on the profiles. The aspect ratio is 4.67.
Paper: Investigations of MWISP Filaments. I. Filament Identification and Analysis Algorithms, and Source Catalog
Authors: Yu Jiang, Xuepeng Chen, Sheng Zheng, Zhibo Jiang, Zhiwei Chen, Yao Huang, Yang Su, Li Sun, Jian-Cheng Feng, Haoran Feng, Ji Yang
Figure caption: All filaments identified from Figure 2. Left panel: velocity-integrated intensity map. The curves of different colors represent the B-spline curves of the intensity skeletons of different filaments, while the numbered circles of different colors indicate the positions of different filaments and retain the same meanings across the three panels. Middle panel: latitude-integrated intensity map. The contours, marked in different colors, illustrate the specific regions of various filaments. Right panel: longitude-integrated intensity map. Except for Filament 7, which has been shown in the previous text, the detailed plots of other filaments are displayed in Figure 15 of Appendix A.
Paper: Investigations of MWISP Filaments. I. Filament Identification and Analysis Algorithms, and Source Catalog
Authors: Yu Jiang, Xuepeng Chen, Sheng Zheng, Zhibo Jiang, Zhiwei Chen, Yao Huang, Yang Su, Li Sun, Jian-Cheng Feng, Haoran Feng, Ji Yang
Figure caption: Cumulative distribution of the velocity separation between filaments identified by DPConCFil and their nearest spiral arm along the line of sight. The spiral arm velocities are depicted in Figure 10. The curves in different colors illustrate the cumulative distributions of velocity differences for the spiral arms. The dark red dashed line represents the cumulative distribution of all velocity differences, while the cyan dashed line depicts the cumulative distribution function of a truncated normal distribution with the same mean and variance as the overall velocity difference.