adsabs/AstroCLIMB huggingface dataset:
Paper: Imaging the Solar Corona from “Within”: A Cutting-edge Approach to Enhance PSP/WISPR Imagery
Authors: Guillermo Stenborg, Angelos Vourlidas, Russell A. Howard
Figure caption: Snapshots of LX-processed WISPR-I images taken during the inbound segment E05 (left panel), and the outbound segments of E07 (middle panel) and E09 (right panel). Each frame was processed with the background models as computed with Equation (1), i.e., Case I, using data from Set 2 (i.e., E17–E21).
Paper: Optimizing Convolution Direction and Template Selection for Difference Image Analysis
Authors: Rodrigo Angulo, Armin Rest, William P. Blair, Jacob Jencson, David A. Coulter, Qinan Wang, Ryan J. Foley, Charles D. Kilpatrick, Xiaolong Li, Anthony L. Piro, César Rojas-Bravo
Figure caption: Left panel: plot of Δm5σ vs. ΔFWHM for YSE field 257A. The color bar is the reduced χ2 of a given difference image. The standard practice would lead to the expectation that a vertical line (red dashed line) would best split the difference image pairs. However, this is clearly not the case. Right panel: similar plot for the same field, but with revised axes: Δm5σ vs. ﹩{10\,{\mathrm{log}}}\,{R}_{{\rm{FWHM}}}﹩. These plots may look similar, but the relation of FWHM is changed to a log ratio, and the color bar is now the Δχ2 with a different color map, which better shows which difference image had the better convolution. The difference images with a better convolution direction or, more accurately, have a lower χ2 are represented by square markers on the plot. The blue dashed line is the best-fit line to divide the better difference images from their counterparts for field 257A.
Paper: Optimizing Convolution Direction and Template Selection for Difference Image Analysis
Authors: Rodrigo Angulo, Armin Rest, William P. Blair, Jacob Jencson, David A. Coulter, Qinan Wang, Ryan J. Foley, Charles D. Kilpatrick, Xiaolong Li, Anthony L. Piro, César Rojas-Bravo
Figure caption: Images for example case a in YSE field 257A. The top two images are the science images (before convolution), and the bottom two images are the difference images of both convolutions. The grayscale limits of the images are 5 times the noise level of the image above and below the background sky value. The science images are scaled to the same photometric zero-point, but have different limits due to different night conditions (e.g., amount of moonlight present). For display purposes, the difference images are scaled to the same limits. These images are all cropped to the same location where one can see how well the stars were subtracted; some objects are labeled with a circle to help with the comparison. D12 has I2 as the template, and D21 has I1 as the template. These images are of comparable seeing and depth, so as expected, both produce similar results. Some residuals are left behind by the star subtraction, shown in the circles, but the effect is small, as can be seen by the low χ2 values.
Paper: Optimizing Convolution Direction and Template Selection for Difference Image Analysis
Authors: Rodrigo Angulo, Armin Rest, William P. Blair, Jacob Jencson, David A. Coulter, Qinan Wang, Ryan J. Foley, Charles D. Kilpatrick, Xiaolong Li, Anthony L. Piro, César Rojas-Bravo
Figure caption: Plot of the contamination for lines of varying slope for each field and the combination of all of the DECam fields. The x-axis is the slope of the line, and the y-axis is the corresponding contamination percentage (see the text). The lines are of form y = mx on a Δm5σ vs. ﹩{10\,{\mathrm{log}}}\,{R}_{{\rm{FWHM}}}﹩ plot, where the slope is in form of ﹩\frac{{\rm{\Delta }}{m}_{5\sigma }}{{10\,{\mathrm{log}}}\,{R}_{{\rm{FWHM}}}}﹩ with units of magnitudes. The minimum of contamination varies slightly per field, but has considerable overlap between 0.35 and 1.47. It is clear a sloped line better divides the plot, hence bolstering the claim that depth of an image has a significant impact on the better direction of convolution.
Paper: Optimizing Convolution Direction and Template Selection for Difference Image Analysis
Authors: Rodrigo Angulo, Armin Rest, William P. Blair, Jacob Jencson, David A. Coulter, Qinan Wang, Ryan J. Foley, Charles D. Kilpatrick, Xiaolong Li, Anthony L. Piro, César Rojas-Bravo
Figure caption: Plots similar to the right panel of Figure 1 for the Swope fields. The colored lines are the respective fields’ best-fit lines that divide the better convolutions from their worse counterparts. The black line represents the global best fit from the DECam fields. Despite the different best-fit slopes, it is clear that a sloped line, not a vertical line, best splits the convolution direction. Hence, image depth is a significant parameter to consider when it comes to image convolution in this case as well.
Paper: Optimizing Convolution Direction and Template Selection for Difference Image Analysis
Authors: Rodrigo Angulo, Armin Rest, William P. Blair, Jacob Jencson, David A. Coulter, Qinan Wang, Ryan J. Foley, Charles D. Kilpatrick, Xiaolong Li, Anthony L. Piro, César Rojas-Bravo
Figure caption: Plot for YSE field 257A as in right panel in Figure 1, but now excluding all data except for the example cases discussed in the text; see Table 6. The example cases sample differing regions of the Δm5σ vs. ﹩{10\,{{\mathrm{log}}}}\,{R}_{{{\rm{FWHM}}}}﹩ plot.
Paper: Optimizing Convolution Direction and Template Selection for Difference Image Analysis
Authors: Rodrigo Angulo, Armin Rest, William P. Blair, Jacob Jencson, David A. Coulter, Qinan Wang, Ryan J. Foley, Charles D. Kilpatrick, Xiaolong Li, Anthony L. Piro, César Rojas-Bravo
Figure caption: Plots of σflux/dflux per magnitude bin measured in the difference images at locations of subtracted stars for example cases a (top) and b (bottom) from YSE field 257A; see Table 6 and Figure 6. Case a (top plot) is for the case with a pair of images with comparable seeing and depth. Case b (bottom plot) is for the case with a pair of images with varying seeing and depth. Clearly, when the χ2 of two difference images are similar, then the σflux/dflux trend is also similar. When χ2 varies, the σflux trend varies as well, with the residual noise much lower in the image with lower χ2.
Paper: Optimizing Convolution Direction and Template Selection for Difference Image Analysis
Authors: Rodrigo Angulo, Armin Rest, William P. Blair, Jacob Jencson, David A. Coulter, Qinan Wang, Ryan J. Foley, Charles D. Kilpatrick, Xiaolong Li, Anthony L. Piro, César Rojas-Bravo
Figure caption: Plots similar to the right panel of Figure 1 for all of the DECam fields, including a plot combining all fields. The colored lines are the respective fields’ best-fit lines that divide the better convolutions from their worse counterparts. The black line represents the global best-fit line when combining all DECam fields; see Table 5 for the slope values. These plots strongly show that a sloped line, not a vertical line, best splits the convolution direction. Hence, depth is a significant parameter to consider when it comes to image convolution.
Paper: A JWST Study of Polycyclic Aromatic Hydrocarbon Emission in a Region of 30 Doradus
Authors: Congcong Zhang, Joelene Hales, Els Peeters, Jan Cami, Ameek Sidhu, Junfeng Zhen
Figure caption: The fractional contribution of each main PAH emission band to the sum of the main bands (i.e., the 3.3, 3.4, 6.2, 7.7, 8.6, 11.2, and 12.7 μm PAH emission bands). The fractional powers for 30 Dor are shown as gray dots, and those of the atomic PDR, DF2, and DF3 in the Orion Bar as blue, green, and red stars, respectively. The average values of 30 Dor are shown by a gray dashed line and a range of one standard deviation is displayed as the gray shaded area.
Paper: A JWST Study of Polycyclic Aromatic Hydrocarbon Emission in a Region of 30 Doradus
Authors: Congcong Zhang, Joelene Hales, Els Peeters, Jan Cami, Ameek Sidhu, Junfeng Zhen
Figure caption: PAH emission in the 3.2–3.6 μm region. Upper panel: comparison of the PAH emission in A11 of 30 Dor with the three Orion Bar templates (the atomic PDR, DF2, and DF3; R. Chown et al. 2024; E. Peeters et al. 2024) shown in the black, blue, green, and red curves respectively, in the 3 μm region. All spectra are normalized to the peak surface brightness of the 3.3 μm PAH emission band. Lower panel: PAH emission in the 3.2–3.6 μm region in 30 Dor for A3, A7, and A11, representative of the variations in our sample in this range.
Paper: A JWST Study of Polycyclic Aromatic Hydrocarbon Emission in a Region of 30 Doradus
Authors: Congcong Zhang, Joelene Hales, Els Peeters, Jan Cami, Ameek Sidhu, Junfeng Zhen
Figure caption: Selected correlations of PAH emission intensities (normalized to the 11.2 μm PAH emission intensity). The intensity ratios for 30 Dor are shown as gray dots, and those of the atomic PDR, DF2, and DF3 in the Orion Bar as a blue, green, and red star, respectively. The 30 Dor aperture number is printed on the right side of the gray dots. The correlation coefficient for the 30 Dor observations is given in the bottom right corner of each panel.
Paper: A JWST Study of Polycyclic Aromatic Hydrocarbon Emission in a Region of 30 Doradus
Authors: Congcong Zhang, Joelene Hales, Els Peeters, Jan Cami, Ameek Sidhu, Junfeng Zhen
Figure caption: The 11.0 and 11.2 μm PAH bands. Upper panel: comparison of the PAH emission in A11 of 30 Dor with the three Orion Bar templates (the atomic PDR, DF2, and DF3; R. Chown et al. 2024; E. Peeters et al. 2024) shown in the black, blue, green, and red curves respectively, in the 11–13 μm region. All spectra are normalized to the peak surface brightness of the 11.2 μm PAH emission band. Lower panel: the 11.0 and 11.2 μm PAH bands in three representative 30 Dor apertures.
Paper: A JWST Study of Polycyclic Aromatic Hydrocarbon Emission in a Region of 30 Doradus
Authors: Congcong Zhang, Joelene Hales, Els Peeters, Jan Cami, Ameek Sidhu, Junfeng Zhen
Figure caption: Example of continuum fitting (see Section 3.7). The continuum is shown for the 3.00–3.75 μm wavelength range associated with the 3.3 μm PAH emission (NIRSpec; top) and for the 4.9–15.5 μm wavelength range (MIRI; bottom). The observations, anchor points, and continuum fit are shown in black, blue, and red, respectively.
Paper: A JWST Study of Polycyclic Aromatic Hydrocarbon Emission in a Region of 30 Doradus
Authors: Congcong Zhang, Joelene Hales, Els Peeters, Jan Cami, Ameek Sidhu, Junfeng Zhen
Figure caption: PAH emission in the 7–9 μm region. Upper panel: comparison of the PAH emission in A11 of 30 Dor with the three Orion Bar templates (the atomic PDR, DF2, and DF3; R. Chown et al. 2024; E. Peeters et al. 2024) shown in the black, blue, green, and red curves respectively, in the 7–9 μm region. All spectra are normalized to the peak surface brightness of the 7.7 μm PAH emission band. Lower panel: PAH emission in the 7–9 μm region in 30 Dor for A3, A10 and A11, representative of the variations in our sample in this range.
Paper: A JWST Study of Polycyclic Aromatic Hydrocarbon Emission in a Region of 30 Doradus
Authors: Congcong Zhang, Joelene Hales, Els Peeters, Jan Cami, Ameek Sidhu, Junfeng Zhen
Figure caption: PAH emission in the 5–7 μm region. Upper panel: comparison of the PAH emission in A11 of 30 Dor with the three Orion Bar templates (the atomic PDR, DF2, and DF3; R. Chown et al. 2024; E. Peeters et al. 2024) shown in the black, blue, green, and red curves respectively, in the 5–7 μm region. All spectra are normalized to the peak surface brightness of the 6.2 μm PAH emission band. Lower panel: the 6.2 μm PAH band in 30 Dor for A3, A7 and A11, representative of the variations in our sample in this range.
Paper: A JWST Study of Polycyclic Aromatic Hydrocarbon Emission in a Region of 30 Doradus
Authors: Congcong Zhang, Joelene Hales, Els Peeters, Jan Cami, Ameek Sidhu, Junfeng Zhen
Figure caption: Gaussian decomposition of the PAH emission in the 3.20–3.62 μm wavelength range for A11. The continuum-subtracted data are shown in black, the Gaussian components are in blue, and the overall fit is in red.
Paper: A JWST Study of Polycyclic Aromatic Hydrocarbon Emission in a Region of 30 Doradus
Authors: Congcong Zhang, Joelene Hales, Els Peeters, Jan Cami, Ameek Sidhu, Junfeng Zhen
Figure caption: Gaussian decomposition of the PAH emission in the 5.95–6.55 μm wavelength range for A11. The continuum-subtracted data are shown in black, the Gaussian components are in blue, and the overall fit is in red.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: Age distribution of cluster member stars. From the left to right panels are the results for Pleiades (M45), NGC 2420, and M67 (NGC 2682). Median age and standard deviation of the individual member stars are marked in the figure. The vertical orange line represents the median age, and the blue line represents the literature age.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: Spatial density and age distribution of our dwarf star sample. The left panels show the stellar distribution in the Galactic coordinate system (l,b), color coded by the stellar density (upper-left panel) and median age (lower-left panel). The right panels show the stellar distribution in the R–Z plane of the Galactic cylindrical coordinate system, color coded by the stellar density (upper-right panel) and median age (lower-right panel). Only stars with a spectral S/N higher than 30 are used to generate this figure.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: Stellar activity S-index as a function of age for stars. The upper-left, upper-right, and bottom-left panels show the results for K dwarfs, G dwarfs, and F dwarfs, respectively. The dots and error bars are the mean and standard deviations of the S-index in different age bins. The bottom-right panel compares the results for these F, G, and K dwarf stars.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: Distribution of the LAMOST star sample. The upper panel (a) shows the distribution in the Teff– log g diagram, color coded by the stellar age estimates. To ensure precise age estimation, here only stars with S/N > 50 are presented. The shaded area masks red giant stars that are not the focus of the current work. The bottom panel (b) shows the age distribution of the full stellar age catalog, with a total number of 4,233,948 stars.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: A comparison of our age estimates with the isochrone ages of M. Xiang et al. (2017). Color represents stellar density normalized to the peak value row-by-row. The white points correspond to the median ages from 1 to 11 Gyr, with the error bars representing the dispersion.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: Measurement error of the logarithmic age as a function of S/N. Results for F/G (5300–7000 K) and K dwarf (4500–5300 K) stars are shown in different symbols and are labeled in the figure. Both measurement errors estimated with a Monte Carlo approach (see the text) and dispersion among repeat observations are presented, as labeled in the figure.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: Relations between age and elemental abundances for K dwarf stars. The abundances are derived from the LAMOST spectra with the DD-Payne (see the text). The dots in white color are the median abundances in individual age bins.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: A comparison of our age estimates with the asteroseismic ages of V. Silva Aguirre et al. (2017). Colors represent the effective temperatures from LAMOST spectra.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: Same as Figure 15, but for abundances of heavier, s- and r-process elements.
Paper: An All-sky 3D Dust Map Based on Gaia and LAMOST
Authors: Tao Wang, 涛 王, Haibo Yuan, 海波 苑, Bingqiu Chen, 丙秋 陈, Maosheng Xiang, 茂盛 向, Ruoyi Zhang, 若羿 张, Bowen Huang, 博闻 黄, Hongrui Gu, 弘睿 顾, Shuaicong Wang, 帅聪 王, Jiawei Li, 佳伟 李
Figure caption: Modeling the extinction–distance relationship. The x-axis represents distance, while the left panels show extinction, and the right panels show the extinction gradient. (a) The blue line represents the extinction model for diffuse dust. The purple segment highlights one of the three free parameters used in the model: the distance from the Sun to the peak extinction surface of the Local Bubble. The red dashed line shows the extinction function without the Local Bubble. (b) The blue line shows the derivative of the extinction function. The red dashed line represents the extinction gradient in the absence of the Local Bubble, while the gold dashed line marks the extinction gradient value at the point where the distance equals zero. This gradient value corresponds to another free parameter used in the model: the maximum density of the diffuse dust. The purple segment indicates the distance at which the dust density falls to 1/e of its maximum value, which corresponds to the projection of the diffuse dust scale height along the line of sight, another key free parameter. (c) The blue line represents the extinction function for the molecular cloud model. The purple segment indicates the cumulative extinction through the molecular cloud, while the green shaded region corresponds to the distance at which approximately 95% of the cumulative extinction is enclosed, defining the parameters related to the molecular cloud size, denoted as ﹩{{\rm{\Lambda }}}_{i}^{\mathrm{MC}}﹩. (d) The blue line shows the derivative of the molecular cloud extinction function. The green dashed line marks the distance where the extinction gradient peaks, defined as the molecular cloud distance. The green shaded region represents the molecular cloud size, as defined in panel (c), while the area beneath the blue line represents the cumulative extinction through the molecular cloud.
Paper: An All-sky 3D Dust Map Based on Gaia and LAMOST
Authors: Tao Wang, 涛 王, Haibo Yuan, 海波 苑, Bingqiu Chen, 丙秋 陈, Maosheng Xiang, 茂盛 向, Ruoyi Zhang, 若羿 张, Bowen Huang, 博闻 黄, Hongrui Gu, 弘睿 顾, Shuaicong Wang, 帅聪 王, Jiawei Li, 佳伟 李
Figure caption: Adaptive multiresolution line-of-sight scheme maximum reliable distance for each line of sight. (a) The number density of the LAMOST and XP sample (see Section 2.4) in the Galactic coordinate system. The number of stars per ∼0.21 deg2 area (Nside =128) is shown. (b) A adaptive multiresolution map created using the data shown in panel (a), in the same coordinate system. The map consists of five distinct HEALPix pixels, each represented by a different color: brown represents a resolution of 54﹩^{\prime} ﹩ (area ∼0.84 deg2), yellow represents 28﹩^{\prime} ﹩ (area ∼0.21 deg2), blue represents 14﹩^{\prime} ﹩ (area ∼0.05 deg2), red represents 6﹩\mathop{.}\limits{^{\prime} }﹩6 (area ∼0.01 deg2), and green represents 3﹩\mathop{.}\limits{^{\prime} }﹩4 (area ∼0.003 deg2). (c) The distribution of the number of stars per line of sight, derived from the adaptive multiresolution scheme shown in panel (b), is color coded according to the stellar count. (d) In the adaptive multiresolution map, the maximum reliable distance for each line of sight is color coded.
Paper: An All-sky 3D Dust Map Based on Gaia and LAMOST
Authors: Tao Wang, 涛 王, Haibo Yuan, 海波 苑, Bingqiu Chen, 丙秋 陈, Maosheng Xiang, 茂盛 向, Ruoyi Zhang, 若羿 张, Bowen Huang, 博闻 黄, Hongrui Gu, 弘睿 顾, Shuaicong Wang, 帅聪 王, Jiawei Li, 佳伟 李
Figure caption: The 2D histogram of the difference between the algorithm-derived E(B − V)LAMOST and E(B − V)SFD as a function of SNRg, with color encoding representing the stellar number density. The data selection is based on E(B − V) < 0.02 mag , ∣b∣ > 20°, and ∣Z∣ > 300 pc. Note that only stars with SNRg < 100 are shown. The red and green lines indicate the standard deviation σ and μ computed within the bins of SNRg, respectively. The blue, yellow-green, and magenta lines represent samples with Teff,LAMOST in the ranges of 5500 K < Teff,LAMOST < 7000 K, Teff,LAMOST < 5500 K, and Teff,LAMOST > 7000 K, respectively. The standard deviation σ is calculated by binning the samples based on their SNRg.
Paper: An All-sky 3D Dust Map Based on Gaia and LAMOST
Authors: Tao Wang, 涛 王, Haibo Yuan, 海波 苑, Bingqiu Chen, 丙秋 陈, Maosheng Xiang, 茂盛 向, Ruoyi Zhang, 若羿 张, Bowen Huang, 博闻 黄, Hongrui Gu, 弘睿 顾, Shuaicong Wang, 帅聪 王, Jiawei Li, 佳伟 李
Figure caption: Examples of the distance–extinction relationship in different line-of-sight directions. Blue points represent E(B − V)LAMOST, orange points represent E(B − V)XP, and gray points represent data that are not within the resolution but included with a certain weight. To avoid overlapping error bars, only 50% of the blue points, 25% of the orange points, and 10% of the gray points have their error bars plotted. “×”-shaped points in different colors represent data removed after 3σ clipping. The red line is the optimal fitting line of our work. The black dashed line indicates the extinction read from the SFD map in this line-of-sight direction. The green line represents the extinction read from the G. M. Green et al. (2019) map.
Paper: An All-sky 3D Dust Map Based on Gaia and LAMOST
Authors: Tao Wang, 涛 王, Haibo Yuan, 海波 苑, Bingqiu Chen, 丙秋 陈, Maosheng Xiang, 茂盛 向, Ruoyi Zhang, 若羿 张, Bowen Huang, 博闻 黄, Hongrui Gu, 弘睿 顾, Shuaicong Wang, 帅聪 王, Jiawei Li, 佳伟 李
Figure caption: Weight function of stellar decentration relative to resolution. The horizontal axis represents the ratio of decentration to resolution, with both decentration and resolution measured in degrees. The blue region indicates decentration within the resolution limit, while the green region represents decentration beyond the resolution limit. The volume of the regions formed by rotating the blue and green areas around the y-axis is approximately equal, implying that, under the assumption of a roughly uniform stellar density, the contributions (weights) from the region within the resolution limit and the region beyond the resolution limit are approximately balanced.
Paper: An All-sky 3D Dust Map Based on Gaia and LAMOST
Authors: Tao Wang, 涛 王, Haibo Yuan, 海波 苑, Bingqiu Chen, 丙秋 陈, Maosheng Xiang, 茂盛 向, Ruoyi Zhang, 若羿 张, Bowen Huang, 博闻 黄, Hongrui Gu, 弘睿 顾, Shuaicong Wang, 帅聪 王, Jiawei Li, 佳伟 李
Figure caption: The 2D histogram of the difference between E(B − V)LAMOST and E(B − V)XP as a function of SNRBP, with color encoding representing the stellar number density. The black line represents the median value in each bin, while the red lines indicate the ±1σ range.