adsabs/AstroCLIMB huggingface dataset:
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: Velocity-integrated intensity images of four clumps in the high overlap area as shown in Figure 20. (A), (B), (C), and (D) correspond to the markers in Figure 20. The red asterisks denote the central locations of the clumps. The green lines denote the principal axis, and the black lines denote the secondary axis. The ratio of the lengths of the principal and secondary axes is equivalent to the ratio of their respective axes. θ denotes the angle between the principal axis and the negative direction of galactic longitude. (l, b, v) is the central coordinate.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: Examples of the synthetic data of Data2 and Data3. The top panel is superimposed by the 13CO emission within 13° ≤ l ≤ 16°, −1.°5 ≤ b ≤ 0.°5, and 0 km s−1 ≤ v ≤ 70 km s−1 and simulated clumps, representing a typical high-density signal environment. The bottom panel is superimposed by the 13CO emission within 184.°5 ≤ l ≤ 187.°5, −1° ≤ b ≤ 1°, and −10 km s−1 ≤ v ≤ 60 km s−1 and simulated clumps, representing a typical low-density signal environment. The cube size is 423 × 241 × 361 voxels, and there are 100 simulated clumps. The red asterisks denote the central locations of the simulated clumps.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: An example of the synthetic data of Data1. It is superimposed by the 13CO emission within 11.°7 ≤ l ≤ 13.°4, 0.°22 ≤ b ≤ 1.°05, and 5 km s−1 ≤ v ≤ 35 km s−1 and simulated clumps. The cube size is 180 × 100 × 200 voxels. The number of simulated clumps is 50, evenly distributed in peak intensities ranging from 0.44 to 16.8 K, sizes ranging from 2 to 4 voxels, and with random angles. The red asterisks denote the central locations of the simulated clumps. The white circles denote the simulated clumps detected by FacetClumps, and the red circles denote the missed simulated clumps. In total, 680 clumps are detected.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The statistics of R of FellWalker, LDC, ConBased, and FacetClumps for synthetic data of different density signal environments. The upper panels are the statistics of synthetic data in a high-density signal environment as shown in the top panel of Figure 22. The lower panels are the statistics of synthetic data in a low-density signal environment as shown in the bottom panel of Figure 22. The left panels depict R as a function of flux, and the right panels depict R as a function of S/N.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The statistics of R of Fellwalker, LDC, ConBased, and FacetClumps for the synthetic data. The left panel shows R as a function of flux, and the right panel shows R as a function of S/N. The lawngreen line is equal to 0.9, and the gold line is equal to 0.8.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The upper panels show the nearest detected clumps of the missed clump centers shown in Figure 15. The lower panels show the undetected simulated clumps. The blue asterisks denote the location of the simulated clumps, and the red asterisks denote the location of the closest detected clumps. ΔX is the distance between the two locations in the graph. The unit of intensity is kelvins.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The statistics of ΔFlux (left) and IOU (right) of FellWalker, LDC, ConBased, and FacetClumps for the synthetic data. ΔFlux and IOU are functions of S/N.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The statistics of ΔX of FellWalker, LDC, ConBased, and FacetClumps for the synthetic data. ΔX is a function of S/N. The left panel shows ΔX in the spatial direction, and the right panel shows ΔX in the velocity channels.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: Distribution of the first derivative F  x  and the first eigenvalue of the region numbered (2) in Figure 2(d). The distribution of F  x  is fitted with the Gaussian function. The thresholds of the first derivative F  x  are the double negative and positive standard deviations (−2 × σ and +2 × σ) of F  x . Coordinates with F  x  value between the thresholds are used to judge the maximum regions. Similarly, the thresholds of F  y  and F  z  are determined in the same manner as F  x . The initial threshold of the first eigenvalue is the x-coordinate of the first bin on the right, and the x-coordinate is shifted one bin to the left after each recursion. Eigenvalues that are less than the threshold are taken into consideration for the judgment. Similarly, the thresholds of the second and third eigenvalues are determined in the same manner as the first eigenvalue.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: (a) The original noise-free data; (b) the original noise-free data with a PPV view; (c) noisy data; (d) the signal regions, with a red box being the smallest enclosing rectangle of a region.
Paper: Survey for Distant Stellar Aggregates in the Galactic Disk: Detecting 2000 Star Clusters and Candidates, along with the Dwarf Galaxy IC 10
Authors: Zhihong He, 治宏 何, Yangping Luo, 杨平 罗, Kun Wang, 坤 王, Anbing Ren, 安炳 任, Liming Peng, 黎明 彭, Qian Cui, 倩 崔, Xiaochen Liu, 效臣 刘, Qingquan Jiang, 青权 蒋
Figure caption: A comparison between the total observed proper-motion dispersions obtained from CG20, represented by gray dots (member stars were crossmatched in Gaia EDR3), and our work, indicated by black plus signs for Type 1 clusters and red crosses for Type 2 clusters. The x-axis in the left plot is presented in a logarithmic scale parallax, while the right plot displays the histograms of the three sets.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: Some of the recursion masks of the region numbered (2) in Figure 2(d). (a) The submaximum region after the first recursion. (b) The submaximum region after the second recursion. (c) The submaximum regions separating one of the clumps and the maximum region of the noise cluster. (d) The submaximum regions separating the last two clumps.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: Flow chart. FacetClumps consists of four subprocesses: (1) signal region extraction based on morphology, (2) clump center detection based on the facet model, (3) local region segmentation based on the gradient, and (4) minimum distance clustering of local regions based on connectivity.
Paper: Survey for Distant Stellar Aggregates in the Galactic Disk: Detecting 2000 Star Clusters and Candidates, along with the Dwarf Galaxy IC 10
Authors: Zhihong He, 治宏 何, Yangping Luo, 杨平 罗, Kun Wang, 坤 王, Anbing Ren, 安炳 任, Liming Peng, 黎明 彭, Qian Cui, 倩 崔, Xiaochen Liu, 效臣 刘, Qingquan Jiang, 青权 蒋
Figure caption: The radial velocity dispersion for newly detected clusters, where blue steps represent Type 1 clusters and orange steps represent Type 2 clusters. Only clusters with more than one matched member star in the Gaia DR3 radial velocity database were included.
Paper: Survey for Distant Stellar Aggregates in the Galactic Disk: Detecting 2000 Star Clusters and Candidates, along with the Dwarf Galaxy IC 10
Authors: Zhihong He, 治宏 何, Yangping Luo, 杨平 罗, Kun Wang, 坤 王, Anbing Ren, 安炳 任, Liming Peng, 黎明 彭, Qian Cui, 倩 崔, Xiaochen Liu, 效臣 刘, Qingquan Jiang, 青权 蒋
Figure caption: Same as Figure 11, but for the apparent radius; the blue and magenta dashed lines indicate the angular sizes corresponding to 5 and 15 pc, respectively.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The integral diagram of the fitting surface of the signal regions shown in Figure 2(d).
Paper: Multiwavelength Bulge–Disk Decomposition for the Galaxy M81 (NGC 3031). I. Morphology
Authors: Jun-Yu Gong, Ye-Wei Mao, Hua Gao, Si-Yue Yu
Figure caption: The Sérsic index (top-left panel), the effective radius (top-right panel), the flux (bottom-left panel), and the flux ratio (bottom-right panel) as a function of wavelength for the bulge (red) and the disk (blue) in M81. In each panel, the Spitzer-IRAC 5.8 μm band is symbolized as the open square. In the top panels, dotted–dashed and dotted lines represent the best-fit quadratic and piecewise linear curves, respectively, for the wave bands with the Spitzer-IRAC 5.8 μm excluded; in the bottom-left panel, dashed lines are the best-fit model spectra of stellar population synthesis; in the bottom-right panel, dashed lines connect data points. Black filled circles in the bottom-left panel indicate the total flux of the galaxy. The flux ratio in the bottom-right panel is defined as the ratio of the bulge (red) or the disk (blue) to the total galaxy enclosed in the same aperture, as well as the ratio of the bulge to the disk (green).
Paper: Multiwavelength Bulge–Disk Decomposition for the Galaxy M81 (NGC 3031). I. Morphology
Authors: Jun-Yu Gong, Ye-Wei Mao, Hua Gao, Si-Yue Yu
Figure caption: The Sérsic index (top row) and the effective radius (bottom row) as a function of wavelength for the bulge (red filled circles) and the disk (blue filled circles), fitted with piecewise linear (left column) and quadratic (right column) curves. In each panel, black dashed and dotted–dashed lines represent the best-fit curves; gray solid lines represent fitting curves obtained from 1000 bootstrap resampling iterations, which define the error ranges for the fits.
Paper: Multiwavelength Bulge–Disk Decomposition for the Galaxy M81 (NGC 3031). I. Morphology
Authors: Jun-Yu Gong, Ye-Wei Mao, Hua Gao, Si-Yue Yu
Figure caption: Morphological parameters, including the Sérsic index (the top-left panel), the effective radius (the top-right panel), the axis ratio (the bottom-left panel), and the PA (the bottom-right panel), as a function of wavelength for the bulge (red color) and the disk (blue color) in M81. In each panel, points are connected by solid lines. In the bottom two panels, dotted lines represent the mean values for the axis ratio (i.e., 0.70 and 0.50 for the bulge and the disk, respectively) and the PA (i.e., −33.°62 and −27.°52 for the bulge and the disk, respectively) among the optical and NIR bands.
Paper: Multiwavelength Bulge–Disk Decomposition for the Galaxy M81 (NGC 3031). I. Morphology
Authors: Jun-Yu Gong, Ye-Wei Mao, Hua Gao, Si-Yue Yu
Figure caption: Images at 20 bands for M81 covering the wavelength range from FUV to NIR, obtained with space- and ground-based telescopes. The physical scale is the same in each of the images. The scale in the bottom-right corner indicates the 5′ length. North is up and east is to the left.
Paper: Survey for Distant Stellar Aggregates in the Galactic Disk: Detecting 2000 Star Clusters and Candidates, along with the Dwarf Galaxy IC 10
Authors: Zhihong He, 治宏 何, Yangping Luo, 杨平 罗, Kun Wang, 坤 王, Anbing Ren, 安炳 任, Liming Peng, 黎明 彭, Qian Cui, 倩 崔, Xiaochen Liu, 效臣 刘, Qingquan Jiang, 青权 蒋
Figure caption: Example of two-Gaussian fitting in the kthNND histogram and the cluster result. (a) CWNU 3455 is detected as a reliable cluster with bound member stars. (b) The k thNND histogram fittings clearly show separation between field stars and member stars of the cluster, along with (c) their low-dispersion proper motions, (d) magnitude-parallax distribution, and (e) clear CMD with reliable isochrone fitting. The cluster also may have many blue straggler stars (BSSs), that could makes it more interesting for researchers. Due to overestimation of BP-band photometry (Riello et al. 2021) in the fainter end of the CMD (especially for G < 19–19.5 mag), the CMD shifts to the bluer side, resulting in unreliable parameters. Therefore, we excluded G > 19.2 mag members when ran the systematic isochrone fits (Section 3.3).
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: Diagram of minimum distance clustering of local regions based on connectivity. N is a continuously updated member of the Fibonacci sequence, and n and T are temporary variables. Connectivity is the necessary condition for merging. In the case of connectivity, a filtered local region is merged into a target region whose clump center is closest to its local center.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: (a) The integrated intensity of the final maximum regions; the white circles represent the valid maximum regions. (b) The clump centers detected by FacetClumps; the background is the signal regions, and the red asterisks denote the locations of the clump centers.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The PP view of some of the local regions and local centers. Each outline delineates the boundary of a local region, the white plus signs denote the local centers, and the red asterisks denote the clump centers.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The statistics of R (left), P (middle), and F 1 (right) curvers of FellWalker, LDC, ConBased, and FacetClumps for the simulated clumps. Black being FellWalker, blue being LDC, brown being ConBased, and red being FacetClumps.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The statistics of ΔX (left), ΔFlux (middle), and IOU (right) curvers of FellWalker, LDC, ConBased, and FacetClumps for the simulated clumps.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The variation trend graph of F 1 with respect to KBins under different S/Ns. The red curve is the mean value of F 1 for all S/Ns.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The PP view of the integral mask of the detected clumps. Each outline delineates the boundary of a clump, and the red asterisks denote the clump centers.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The variation trend graph of the mean value of F 1 with respect to SWindow.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The PPV view of the detected clumps. Different colored voxels denote different clumps.
Paper: Ionized-gas Metallicity of the Strong [O III]λ5007 Emission-line Compact Galaxies in the LAMOST Survey
Authors: Siqi Liu, 思琦 刘, A-Li Luo, 阿理 罗, Wei Zhang, 伟 张, Xiao Kong, 啸 孔, Yan-Xia Zhang, 彦霞 张, Shi-Yin Shen, 世银 沈, Yong-Heng Zhao, 永恒 赵
Figure caption: The N/O vs. O/H relation of the strong [O III]λ5007 emission-line compact galaxies. The colors of the scatter points mark the flux ratio of Hα to Hβ. The measurements of the stacked spectra in bins of stellar mass and SFR from Andrews & Martini (2013) at z ∼ 0 are marked with gray stars. The blue curve is the prediction from Andrews & Martini (2013). The green curve is the fitting result from the KLEVER survey at z ∼ 2 (Hayden-Pawson et al. 2022).
Paper: Ionized-gas Metallicity of the Strong [O III]λ5007 Emission-line Compact Galaxies in the LAMOST Survey
Authors: Siqi Liu, 思琦 刘, A-Li Luo, 阿理 罗, Wei Zhang, 伟 张, Xiao Kong, 啸 孔, Yan-Xia Zhang, 彦霞 张, Shi-Yin Shen, 世银 沈, Yong-Heng Zhao, 永恒 赵
Figure caption: The set of strong-line calibrations from Maiolino et al. (2008), Jones et al. (2015), and Curti et al. (2020) (black curve) and Nakajima et al. (2022) (brown curve). The x-axis marks the metallicity obtained from the direct-T  e  method. The distribution of the strong [O III]λ5007 emission-line compact galaxies is displayed with blue scatter points. The crimson stars mark the three galaxies from JWST at z ∼ 7.7−8.5 with the measurements from Curti et al. (2023).
Paper: Ionized-gas Metallicity of the Strong [O III]λ5007 Emission-line Compact Galaxies in the LAMOST Survey
Authors: Siqi Liu, 思琦 刘, A-Li Luo, 阿理 罗, Wei Zhang, 伟 张, Xiao Kong, 啸 孔, Yan-Xia Zhang, 彦霞 张, Shi-Yin Shen, 世银 沈, Yong-Heng Zhao, 永恒 赵
Figure caption: Asymmetric [O III]λ5007 emission-line profiles of the first four sources. The black step plot is the recalibrated LAMOST spectra with the continuum subtracted. The error region from the inverse variance is marked with the gray-shaded regions. The central wavelengths from the spectral fit and the velocity dispersions of the profiles are marked above the emission lines. The velocity separation of the [O III]λ5007 emission line is marked on the right part of the plot.
Paper: Ionized-gas Metallicity of the Strong [O III]λ5007 Emission-line Compact Galaxies in the LAMOST Survey
Authors: Siqi Liu, 思琦 刘, A-Li Luo, 阿理 罗, Wei Zhang, 伟 张, Xiao Kong, 啸 孔, Yan-Xia Zhang, 彦霞 张, Shi-Yin Shen, 世银 沈, Yong-Heng Zhao, 永恒 赵
Figure caption: Asymmetric [O III]λ4959 and [O III]λ5007 emission-line profiles for each single exposure of 811511143. The black step plot is the original 1D spectra with the sky emission subtracted. The error region from the inverse variance is marked with the gray-shaded regions. The central wavelengths from the spectral fit and the velocity dispersions of the profiles are marked above the emission lines. The velocity separations of the [O III]λ4959 and [O III]λ5007 emission lines are marked on the right part of the plot.
Paper: Ionized-gas Metallicity of the Strong [O III]λ5007 Emission-line Compact Galaxies in the LAMOST Survey
Authors: Siqi Liu, 思琦 刘, A-Li Luo, 阿理 罗, Wei Zhang, 伟 张, Xiao Kong, 啸 孔, Yan-Xia Zhang, 彦霞 张, Shi-Yin Shen, 世银 沈, Yong-Heng Zhao, 永恒 赵
Figure caption: The relation of the Ne3O2 vs. stellar mass of the strong [O III]λ5007 emission-line compact galaxies, where the colors of the scatter points mark the redshift of the source. The samples at z ∼ 0 from Andrews & Martini (2013) are marked with gray stars, the stacked measurement results with the dust corrected from Jeong et al. (2020) are marked with dark cyan diamonds, the stacked measurement results from the Jeong et al. (2020) are marked with purple squares (Zeimann et al. 2015). The red stars mark the three galaxies from JWST.
Paper: Ionized-gas Metallicity of the Strong [O III]λ5007 Emission-line Compact Galaxies in the LAMOST Survey
Authors: Siqi Liu, 思琦 刘, A-Li Luo, 阿理 罗, Wei Zhang, 伟 张, Xiao Kong, 啸 孔, Yan-Xia Zhang, 彦霞 张, Shi-Yin Shen, 世银 沈, Yong-Heng Zhao, 永恒 赵
Figure caption: Ne3O2 vs. O32 relation. The strong [O III]λ5007 emission-line compact galaxies are demonstrated with brown error bars. The samples at z ∼ 0 from Andrews & Martini (2013) are marked with gray stars. The stacked samples with the dust corrected from Jeong et al. (2020) are marked with dark cyan diamonds. The red stars mark the three galaxies from JWST.
Paper: Ionized-gas Metallicity of the Strong [O III]λ5007 Emission-line Compact Galaxies in the LAMOST Survey
Authors: Siqi Liu, 思琦 刘, A-Li Luo, 阿理 罗, Wei Zhang, 伟 张, Xiao Kong, 啸 孔, Yan-Xia Zhang, 彦霞 张, Shi-Yin Shen, 世银 沈, Yong-Heng Zhao, 永恒 赵
Figure caption: The mass–metallicity relation of the strong [O III]λ5007 emission-line compact galaxies. Different mass–metallicity relations from Tremonti et al. (2004), Yates et al. (2020), and Curti et al. (2020) are marked with curves with the corresponding colors as noted in the legend. The crimson stars mark the three galaxies from JWST at z ∼ 7.7−8.5 with the measurements from Curti et al. (2023).
Paper: Ionized-gas Metallicity of the Strong [O III]λ5007 Emission-line Compact Galaxies in the LAMOST Survey
Authors: Siqi Liu, 思琦 刘, A-Li Luo, 阿理 罗, Wei Zhang, 伟 张, Xiao Kong, 啸 孔, Yan-Xia Zhang, 彦霞 张, Shi-Yin Shen, 世银 沈, Yong-Heng Zhao, 永恒 赵
Figure caption: The distribution of the derived n  e .
Paper: Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
Authors: R. Hložek, A. I. Malz, K. A. Ponder, M. Dai, G. Narayan, E. E. O. Ishida, T. Allam Jr, A. Bahmanyar, X. Bi, R. Biswas, K. Boone, S. Chen, N. Du, A. Erdem, L. Galbany, A. Garreta, S. W. Jha, D. O. Jones, R. Kessler, M. Lin, J. Liu, M. Lochner, A. A. Mahabal, K. S. Mandel, P. Margolis, J. R. Martínez-Galarza, J. D. McEwen, D. Muthukrishna, Y. Nakatsuka, T. Noumi, T. Oya, H. V. Peiris, C. M. Peters, J. F. Puget, C. N. Setzer, Siddhartha, S. Stefanov, T. Xie, L. Yan, K.-H. Yeh, W. Zuo
Figure caption: The estimated posterior probabilities (violins) for each predicted class (columns) among a given true class (panels) for the Kyle Boone method, where the assigned class corresponding to the true class is highlighted in color. Top row: the estimated probabilities for well-classified SN Ia and those of the less well-classified SN Iax. The assigned probability of being the true class for SN Ia has a long tail but a high mode of ∼0.8, whereas SN Iax is degenerate with the “Other” class type, whose mode is ∼0.2. Bottom row: the classification probabilities for SN Ia-91bg and SN Ibc (core-collapse SN). The estimated classification probabilities for true SN Ia-91bg objects reflects uncertainty; while roughly two-thirds of the true SN Ia-91bg objects have a mode corresponding to the predicted SN Ia-91bg class, their classification probabilities span the entire range of probability. The uncertainty of estimated probabilities for SN Ibc objects manifests differently; the probability for predicted class SN Ibc has a less pronounced tail, but there is considerable probability mass assigned to the predicted “Other” class. This is not surprising given that the Kyle Boone classifier determined the “Other” class classification probability from a combination of the probabilities of the SN Ia, SN Ibc, and SN II.