adsabs/AstroCLIMB huggingface dataset:
Paper: BCDDM: Branch Correction Denoising Diffusion Model for Black Hole Image Generation
Authors: Ao Liu, Zelin Zhang, Songbai Chen, Cuihong Wen, Jieci Wang
Figure caption: The diffusion process of black hole images. We output the image every 100 steps for a total of 1000 times to observe the changes in the diffusion process of the image.
Paper: BCDDM: Branch Correction Denoising Diffusion Model for Black Hole Image Generation
Authors: Ao Liu, Zelin Zhang, Songbai Chen, Cuihong Wen, Jieci Wang
Figure caption: The binary confusion matrix of Fdir. The data sets corresponding to the three images are RLDs (left), FKDs (middle), and MXDs (right). The classification accuracies of three training sets for fluid direction are 92.19%, 89.58%, and 94.27%.
Paper: BCDDM: Branch Correction Denoising Diffusion Model for Black Hole Image Generation
Authors: Ao Liu, Zelin Zhang, Songbai Chen, Cuihong Wen, Jieci Wang
Figure caption: The fitting results of RLDs and MXDs on the test set samples. The prediction error of the test set after training with RLDs for 5000 epochs (left) and the prediction error of the test set after training with MXDs for 5000 epochs (right).
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: The PANCAKE workflow includes three main tasks (template CMD generation, CMD gridding, and CMD fitting) and several assistant tasks (preprocessing and foreground star CMD generation and removal). A general description is given at the beginning of Section 2. A detailed introduction to each main task is given in the following sections.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: The best-fit output age–metallicity maps for three gridding methods. The example mock CMD has constant SFH and constant metallicity. We use the Kroupa IMF and a binary fraction of 35%. The uniform binning has 100 bins on both the color and magnitude axes. The thresholds of the quadtree and Voronoi binnings vary from 10 to 40. The normalized χ2 values (in Figure 7) quantitatively show “Quadtree 5” as the best gridding recipe, here labeled in red.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: Schematic diagram of the Milky Way foreground star removal. The black dots in the first and second images show the original observed CMD of NGC 6822, which is a good example due to its large number of foreground stars. The blue points in the second panel represent the model CMD of foreground stars. To remove these foreground stars from the original observed CMD, we implement a removal criterion with δmag < 1.5δmax, see text for details. The third panel shows the observed CMD after removing the foreground stars.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: Schematic diagram of isochrone template CMD generation. The black points represent the CMD of the Fornax dwarf galaxy. The red points represent the isochrone template with ﹩\mathrm{log}(t/\mathrm{yr})=9.95﹩ (t = 8.91 Gyr) and [M/H] = −1.3. The three panels from left to right illustrate the steps of template CMD generation: (1) randomly sampling the 104 masses according to the IMF, linearly interpolating the mass–luminosity relation provided by the theoretical isochrone, and convolving with the target distance and extinction; (2) randomly sampling 35% binaries and adding companions; and (3) incorporating observational flux errors and completeness.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: Schematic diagram of the three gridding methods, uniform binning, quadtree binning, and Voronoi binning. The background black points are the Fornax dwarf galaxy. Uniform binning has 20 bins in both color and magnitude axes, while quadtree binning and Voronoi binning have a threshold of 40 star counts.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: Systematic uncertainties of the gridding methods, binary fractions, IMFs, and the uncertainty in star counts (age–metallicity map densities). The uncertainties in the figure exclude the basic uncertainties (see text), therefore only the uncertainties themselves are included and quantified.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: The best-fit output age–metallicity maps for various IMFs and binary fractions. The example mock CMD has constant SFH and constant metallicity, and is built based on the Kroupa IMF and a binary fraction of 35%. “K,” “CI,” and “CS” denote the Kroupa, Chabrier individual, and Chabrier system IMFs, respectively. The binary fractions range from 10% to 70%. The true age–metallicity map of the example mock is shown in the upper-left panel of Figure 5. When the IMF and binary fraction are the same as the input (Kroupa IMF and 35% binary fraction; labeled in red), the fitting result is the best.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: The Δ cumulative SFH in mocks with ∼11,000 points, where the PANCAKE input IMF and binary fraction are the same as the mocks, and using quadtree binning with a threshold of five as suggested in Section 3.1. The left panel shows the mocks with a logarithmic age x-axis in years, and the right panel shows the mocks with an age x-axis in Gyr.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: Systematic uncertainties for varying logarithmic age bins, logarithmic age ranges, and metallicity bins using the PARSEC isochrones. The left panel has logarithmic age on the x-axis, while the right panel has age in units of Gyr. The top panel shows the results for different (13, 26, and 51) logarithmic age bins, the middle panel shows the effect of different initial values (6.6, 7.48, and 8.4) for the logarithmic age bins, and the bottom panel shows the results for different (6, 12, and 24) metallicity bins.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: The normalized χ2 values for various thresholds of quadtree binning. The mocks are in gray, the mean is in orange, while the median and STD values are in blue.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: The normalized χ2 values for various IMFs and binary fractions. The mean values of the normalized χ2 values are presented in the color bar and labeled with errors. When the IMF and binary fraction are the same as the input (Kroupa IMF and a 35% binary fraction; labeled in red), the fitting result is the best.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: The normalized χ2 values for the various gridding methods. The mocks are in gray, the mean is in orange, while the median and STD values are in blue. The relative best gridding recipe (quadtree with a threshold of 10) is labeled in red.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: The normalized χ2 values for various star counts in the mock CMDs. The mocks are in gray, the mean is in orange, while the median and STD values are in blue.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: Systematic uncertainties for various isochrones, Dartmouth, BaSTI, and PARSEC. Different isochrones lead to the largest systematic uncertainties in Δ cumulative SFH, ranging between −0.2 and 0.3.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: The cumulative SFHs of dwarf galaxies, including D. R. Weisz et al. (2014a) in blue with random uncertainties and this paper in red. We divide the galaxies into Group A and Group B. Group A shows agreement between D. R. Weisz et al. (2014a) and this paper, while Group B shows offsets between them. Group B is further divided into three classes, see text for details. For each class in Group B, an example of CMD distribution (black points) is given in the figure. Sagittarius [u37501] is a special example, which not only suffers from lower star counts but is also strongly affected by foreground stars. For Sagittarius, the foreground stars model gives 850 star counts, already occupying 20.9% of the total of 4059 star counts.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: Atlas of files during the transformation of the PANCAKE fitting results, using the DDO 210 dwarf as an example. The first row shows the number of stars to mass ratio map (SFM-to-S) and the observational completeness map (C). The second row shows the fitting output weight matrix W and the output age–metallicity map in mass (SFM). The third row shows the cumulative SFH, SFR, and age–metallicity relation, compared with A. A. Cole et al. (2014), see discussion in Section 5.4.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: Atlas of files used during the processing of PANCAKE, using the DDO 210 dwarf as an example. The first row shows the observed CMD, examples of the template CMDs, the foreground star CMD, and the observed CMD after foreground star removal. The second row shows the artificial star results of two filters. The third row shows the PANCAKE model CMD and the 2D histogram figures for the observed CMD after foreground star removal, the PANCAKE model CMD, and the residual CMD (observed – model).
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: The cumulative SFHs of DDO 210. Same as Figure 19, D. R. Weisz et al. (2014a) is presented in blue with an error region and the result of this paper is in red. The result of A. A. Cole et al. (2014) is in green with dots. A. A. Cole et al. (2014) used a deeper observation from HST-ACS/WFC that contains the main-sequence region of DDO 210.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: The cumulative SFHs of Fornax. As in Figure 18, the result of D. R. Weisz et al. (2014a) is presented with a random uncertainty error region, while the other works are shown as lines of different colors labeled in the figure. The reference, observational information, and CMD method input parameters of all the results are listed in Tables 2 and 3.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: The age–metallicity map of NGC 1755 with the color bar showing the number of stars. The result shows two different populations, circled in red (Population I) and blue (Population II). The model CMDs of populations I and II are shown in the same color in Figure 17.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: The observed and model CMDs of NGC 1755. The observed CMD points are in black, while the two populations of model CMD points are in red and blue, respectively. The bottom panels show the two populations with the average logarithmic age and metallicity labeled.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: The fitting result for NGC 1755. Three panels are the observation, model, and residual CMD intensity maps with the color bar showing star count. Residual = observation − model. The fourth panel shows a histogram of the residuals. The black line shows the mean value of the residuals, which is 6.79 × 10−17, while the red dashed lines show the STD (1σ = 1.53) and the black dashed lines show 3σ = 4.58.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: A comparison between the FUV SFR and CMD SFR at 100 Myr (blue crosses) and 300 Myr (red dots). The black dashed line represents the 1:1 line.
Paper: PANCAKE: A Python-based Numerical Color–Magnitude Diagram Analysis Package
Authors: Yun Zheng, Yujiao Yang, Yong-Kun Zhang, Zheng Zheng, Jing Wang, Lister Staveley-Smith, Chao-Wei Tsai, Di Li, Chao Liu, Jingjing Hu, Huaxi Chen, Donghui Quan, Yinghui Zheng, Hangyuan Li
Figure caption: Atlas of files during the GALEX photometry, using the DDO 210 dwarf as an example. The four panels show the cut FUV and NUV original data, the FUV data after background removal and masking, and the SFR map. The orange region in the fourth panel shows the WFPC2/HST field of view.
Paper: Insights into the X-Ray Burst Pulse Morphology from SGR J1935+2154
Authors: Jiao-Jiao Yang, Shuo Xiao, Yue Wang, Shao-Lin Xiong, Lin Lin, Zheng-Huo Jiang, Tong-Lei Liao, Xiao-Bo Li, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Shuang-Nan Zhang
Figure caption: Left: the MXB light curve from SGR J1935+2154 observed by GBM (ObsID 210911647). The dashed lines indicate the pulses identified by the pulse-finding algorithm at different time resolutions, while the red solid line represents the result of fitting these pulses using multiple FRED functions. Right: the distribution of the number of pulses from different detector MXBs (GBM, GECAM, and HXMT), with the vertical dashed line representing the peak of the distribution, which is 1 for all three.
Paper: Insights into the X-Ray Burst Pulse Morphology from SGR J1935+2154
Authors: Jiao-Jiao Yang, Shuo Xiao, Yue Wang, Shao-Lin Xiong, Lin Lin, Zheng-Huo Jiang, Tong-Lei Liao, Xiao-Bo Li, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Shuang-Nan Zhang
Figure caption: The left and right panels show the distribution of MVT and duration for SGR J1935+2154 MXBs observed by GBM, GECAM, and HXMT. Each subsample (green, purple, and red correspond to GBM, GECAM ,and HXMT, respectively) is well fitted by a log-Gaussian function (solid curve), with the dashed lines indicating the fitted mean values.
Paper: Insights into the X-Ray Burst Pulse Morphology from SGR J1935+2154
Authors: Jiao-Jiao Yang, Shuo Xiao, Yue Wang, Shao-Lin Xiong, Lin Lin, Zheng-Huo Jiang, Tong-Lei Liao, Xiao-Bo Li, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Shuang-Nan Zhang
Figure caption: The scatter points in different colors represent the measurements from different satellites (GBM, GECAM, and HXMT). Left: the scatter plot shows the relationship between MVT and the number of pulses. Right: the MVTs estimated by the pulse-fitting method Δtmvt,25 (this work) and the BB method Δtmvt,BB are compared for a common sample observed by different satellites, with the latter results taken from S. Xiao et al. (2023b). The black dashed line denotes the equality line.
Paper: Insights into the X-Ray Burst Pulse Morphology from SGR J1935+2154
Authors: Jiao-Jiao Yang, Shuo Xiao, Yue Wang, Shao-Lin Xiong, Lin Lin, Zheng-Huo Jiang, Tong-Lei Liao, Xiao-Bo Li, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Shuang-Nan Zhang
Figure caption: The left and right panels show the distribution of pulse skewness and peakedness of MXBs observed by GBM, GECAM, and HXMT. Each subsample (green, purple, and red correspond to GBM, GECAM, and HXMT, respectively) is well fitted by a log-Gaussian function (solid curve), with the dashed lines indicating the fitted mean values.
Paper: Insights into the X-Ray Burst Pulse Morphology from SGR J1935+2154
Authors: Jiao-Jiao Yang, Shuo Xiao, Yue Wang, Shao-Lin Xiong, Lin Lin, Zheng-Huo Jiang, Tong-Lei Liao, Xiao-Bo Li, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Shuang-Nan Zhang
Figure caption: The top (middle/bottom) panel displays the distributions of rise time (decay time/width) of MXBs. The solid lines represent the fitted curves using a log-Gaussian function and the vertical dashed lines indicate the fitted mean values. The green, purple, and red colors correspond to the data from GBM, GECAM, and HXMT, respectively.
Paper: Insights into the X-Ray Burst Pulse Morphology from SGR J1935+2154
Authors: Jiao-Jiao Yang, Shuo Xiao, Yue Wang, Shao-Lin Xiong, Lin Lin, Zheng-Huo Jiang, Tong-Lei Liao, Xiao-Bo Li, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Shuang-Nan Zhang
Figure caption: Correlations among pulse parameters (amplitude, peakedness, width, skewness, and rise/decay time) of MXBs from SGR J1935+2154 detected by GECAM. The power-law fits (black) characterize these relationships as y = bxa, where a represents the power-law index and b denotes the proportionality constant.
Paper: Insights into the X-Ray Burst Pulse Morphology from SGR J1935+2154
Authors: Jiao-Jiao Yang, Shuo Xiao, Yue Wang, Shao-Lin Xiong, Lin Lin, Zheng-Huo Jiang, Tong-Lei Liao, Xiao-Bo Li, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Shuang-Nan Zhang
Figure caption: The top (bottom) panel shows the relationship between the duration of the MXBs and the MVT or number of pulses, where the red, blue, and green points represent data from each instrument (GBM, GECAM, and HXMT, respectively).
Paper: Insights into the X-Ray Burst Pulse Morphology from SGR J1935+2154
Authors: Jiao-Jiao Yang, Shuo Xiao, Yue Wang, Shao-Lin Xiong, Lin Lin, Zheng-Huo Jiang, Tong-Lei Liao, Xiao-Bo Li, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Shuang-Nan Zhang
Figure caption: Correlations among pulse parameters (amplitude, peakedness, width, skewness, and rise/decay time) of MXBs from SGR J1935+2154 detected by GBM. The power-law fits (black) characterize these relationships as y = bxa, where a represents the power-law index and b denotes the proportionality constant.
Paper: Insights into the X-Ray Burst Pulse Morphology from SGR J1935+2154
Authors: Jiao-Jiao Yang, Shuo Xiao, Yue Wang, Shao-Lin Xiong, Lin Lin, Zheng-Huo Jiang, Tong-Lei Liao, Xiao-Bo Li, Cheng-Kui Li, Shu-Xu Yi, Zhen Zhang, Shuang-Nan Zhang
Figure caption: The distribution of pulse-waiting times from different detector MXBs (GBM, GECAM, and HXMT). The green, purple, and red curves represent the log-Gaussian function fitted curves for each subsample, with the dashed line indicating the position of the fitted mean.
Paper: The Extended Atlas of Low-resolution Spectra from the Infrared Astronomical Satellite
Authors: G. C. Sloan, Kathleen E. Kraemer, K. Volk
Figure caption: The ratio of the flux density at 12 μm of the dust-free stars in the LRS Atlas from the spectra over the color-corrected photometry from the IRAS PSC. The large black circles give the averages in each half-decade bin, with horizontal error bars indicating the standard deviation and vertical error bars indicating the uncertainty in the mean. The vertical standard deviation is apparent from the spread in the individual data.
Paper: The Extended Atlas of Low-resolution Spectra from the Infrared Astronomical Satellite
Authors: G. C. Sloan, Kathleen E. Kraemer, K. Volk
Figure caption: A comparison of LRS spectra of the A dwarf α CMa and the K giant α Tau with spectra from the Spitzer/IRS of standard stars with similar spectral types. The LRS spectra are plotted with and without the spectral correction derived by M. Cohen et al. (1992a; i.e., the Cohen correction). In the Rayleigh–Jeans units used, stellar spectra with no absorption bands should be almost horizontal, with a gentle rise from the blue end, as δ UMi illustrates. The spectra from the IRS are down-sampled to the resolution of the LRS and scaled.
Paper: The Extended Atlas of Low-resolution Spectra from the Infrared Astronomical Satellite
Authors: G. C. Sloan, Kathleen E. Kraemer, K. Volk
Figure caption: The uncorrected LRS spectrum of Procyon (α CMi), compared to a scaled BOSZ model and the photometry from the PSC. The LRS spectrum shows the emission artifact at 8–11 μm in the original LRS Atlas.
Paper: The Extended Atlas of Low-resolution Spectra from the Infrared Astronomical Satellite
Authors: G. C. Sloan, Kathleen E. Kraemer, K. Volk
Figure caption: Testing the spectral correction derived from red giants. While the recovered shape of the SiO absorption band at ∼8–11 μm has improved in α Tau, the shape of the spectrum of α CMa has grown worse in the same wavelength range, especially at the blue end of the spectrum.