adsabs/AstroCLIMB huggingface dataset:
Paper: The XMM-Newton Line Emission Analysis Program (X-LEAP). I. Emission-line Survey of O VII, O VIII, and Fe L-shell Transitions
Authors: Zeyang Pan, Zhijie Qu, Joel N. Bregman, Jifeng Liu
Figure caption: Left: the O VII, O VIII, and Fe-L line emissivities at different APEC temperatures (Foster et al. 2012). They are disabled in the APEC model to prevent double-counting line intensities when extracting them using Gaussian functions. Right: the emissivity ratios. The observed line intensity ratios are consistent with the emissivity ratios if the emission originates from a single-temperature plasma.
Paper: The XMM-Newton Line Emission Analysis Program (X-LEAP). I. Emission-line Survey of O VII, O VIII, and Fe L-shell Transitions
Authors: Zeyang Pan, Zhijie Qu, Joel N. Bregman, Jifeng Liu
Figure caption: Top row: comparison of shared O VII and O VIII intensity measurements between this work and HS12. Bottom row: weighted differences derived from intensity difference (ΔI) divided by total uncertainty (σ). Interpretation of the differences is given in Section 4.4.
Paper: StellarGAN: Classifying Stellar Spectra with Generative Adversarial Networks in SDSS and APOGEE Sky Surveys
Authors: Wei Liu, Shuo Cao, Xian-Chuan Yu, Meng Zhu, Marek Biesiada, Jiawen Yao, Minghao Du
Figure caption: Comparative heat map of SDSS and APOGEE data sets. The left panels cover different types of stars from SDSS (from top to bottom: O, B, A, F, G, K, and M types), and the right panels cover different types of stars from APOGEE (from top to bottom: A, F, G, K, and M types). Color variations represent the model’s varying attention on specific spectral wavelengths.
Paper: Detection of Pairwise Kinetic Sunyaev–Zel’dovich Effect with DESI Galaxy Groups and Planck in Fourier Space
Authors: Shaohong Li, Yi Zheng, Ziyang Chen, Haojie Xu, Xiaohu Yang
Figure caption: Left: Multipoles of the survey window function for the baseline sample, which are used to compute the masked theoretical pairwise kSZ power dipole. Solid and dashed lines indicate the monopole and qradrupole separately. The red and blue lines represent the window functions of NGC and SGC respectively. Right: Masked (solid lines) and unmasked (dashed lines) theoretical predictions of the pairwise kSZ dipole for the baseline sample. The red and blue lines are for NGC and SGC respectively. On the lower panel, the relative difference Diff = P mask/P nomask − 1 induced by the window function effect is presented. The inflation of “Diff” at k ∼ 0.15h Mpc−1 is due to the zero-crossing of ﹩{P}_{\mathrm{kSZ}}^{{\ell }=1}﹩ at that scale.
Paper: Detection of Pairwise Kinetic Sunyaev–Zel’dovich Effect with DESI Galaxy Groups and Planck in Fourier Space
Authors: Shaohong Li, Yi Zheng, Ziyang Chen, Haojie Xu, Xiaohu Yang
Figure caption: The mass, redshift, angular radius, and richness distribution of the baseline sample groups. The median values of {M, z, θ halo, richness} are ﹩\{1.75\times {10}^{13}\ {M}_{\odot }/h,0.64,1\buildrel{\,\prime}\over{.} 5,3\}﹩ for both NGC (red solid lines) and SGC (blue solid lines) groups. The means of these properties are presented by the dashed lines.
Paper: Detection of Pairwise Kinetic Sunyaev–Zel’dovich Effect with DESI Galaxy Groups and Planck in Fourier Space
Authors: Shaohong Li, Yi Zheng, Ziyang Chen, Haojie Xu, Xiaohu Yang
Figure caption: Theoretical predictions of the unmasked pairwise kSZ multipoles of the NGC subsample of the baseline catalog. Solid lines represent multipoles of the nonlinear density-weighted pairwise kSZ power spectrum with photo-z errors (Equation (17)), and dashed lines are those without photo-z errors (Equation (14)). The red and blue lines are for dipoles (ℓ = 1) and octopoles (ℓ = 3) respectively. The adopted redshift is the median redshift ﹩\tilde{z}=0.64﹩ of the NGC subsample, and ﹩\bar{\tau }=3.96\times {10}^{-5}﹩ is the best-fitted ﹩\bar{\tau }﹩ value of the baseline sample.
Paper: Detection of Pairwise Kinetic Sunyaev–Zel’dovich Effect with DESI Galaxy Groups and Planck in Fourier Space
Authors: Shaohong Li, Yi Zheng, Ziyang Chen, Haojie Xu, Xiaohu Yang
Figure caption: The sky distribution of groups (left) and random points (right) of the baseline sample shown in the Galactic coordinate with N side = 256 in the HEALPix grid frame. The color bar represents the number of groups or random points in each pixel. There are ∼5.65 × 106 groups in the baseline sample, and the random catalog size is 20 times the corresponding group sample.
Paper: The XMM-Newton Line Emission Analysis Program (X-LEAP). I. Emission-line Survey of O VII, O VIII, and Fe L-shell Transitions
Authors: Zeyang Pan, Zhijie Qu, Joel N. Bregman, Jifeng Liu
Figure caption: O VII intensity variations in ecliptic latitude. Top: median O VII intensities derived from “rebin” and “close-pair” methods in low and high ecliptic latitudes (∣β∣ < 30° and ∣β∣ ≥ 30°) over 1 yr bins. Bottom: the intensity differences and 1σ uncertainty from the “close-pair” result, along with smoothed SSN. The O VII intensity is generally higher at higher ecliptic latitudes, especially during solar maxima. This variation likely results from the varying solar wind properties at different latitudes.
Paper: The XMM-Newton Line Emission Analysis Program (X-LEAP). I. Emission-line Survey of O VII, O VIII, and Fe L-shell Transitions
Authors: Zeyang Pan, Zhijie Qu, Joel N. Bregman, Jifeng Liu
Figure caption: Left column: O VII intensity measurements/upper limits obtained from observations of the same targets within 1 month, 1 day, and 2 days, respectively. The gray lines represent the 12 and 1 hr averaged proton fluxes retrieved from ACE. Right column: the corresponding FOVs for these observation sets. The AA Tauri observations maintain the same direction, while the Mars observations shift by ≈0.°5. Significant variations in the top two rows are likely associated with the magnetospheric SWCX.
Paper: Relations of Rotation and Chromospheric Activity to Stellar Age for FGK Dwarfs from Kepler and LAMOST
Authors: Lifei Ye, Shaolan Bi, Jinghua Zhang, Tiancheng Sun, Liu Long, Zhishuai Ge, Tanda Li, Xianfei Zhang, Xunzhou Chen, Yaguang Li, Jianzhao Zhou, Maosheng Xiang
Figure caption: Distribution of rotation period with age for F, G, and K dwarfs. The segment linear fitting is represented by lines, with line colors corresponding to spectral type and line styles denoting distinct metallicity, as noted in the text. Scatter sizes as well as color reflect the data distribution density. The solar P rot (24.47 ∼ 35 days) and age (∼4.57 Gyr) are marked by a red error bar in the central panels. Blue error bars are the typical uncertainty of each bin. Triangles represent the ﹩\mathrm{log}{S}_{\mathrm{ph}}\gt 3.3﹩ in diffuse points (the data distribution density less than 16%).
Paper: Relations of Rotation and Chromospheric Activity to Stellar Age for FGK Dwarfs from Kepler and LAMOST
Authors: Lifei Ye, Shaolan Bi, Jinghua Zhang, Tiancheng Sun, Liu Long, Zhishuai Ge, Tanda Li, Xianfei Zhang, Xunzhou Chen, Yaguang Li, Jianzhao Zhou, Maosheng Xiang
Figure caption: Comparison of fundamental parameters between this work and the asteroseismic sample. The x-axis displays the mass and age values calculated using asteroseismic parameters. The solid lines represent the equal values of the parameters. Different symbols are used for different pipelines: solid triangles for AST, hollow triangles for BASTA, red spots for C2kSMO, circles for GOE, solid squares for V&A, hollow squares for YMCM, and black spots for Serenelli17. The short gray line is the error bar. The residuals are plotted on the y-axis in the respective lower panels.
Paper: Relations of Rotation and Chromospheric Activity to Stellar Age for FGK Dwarfs from Kepler and LAMOST
Authors: Lifei Ye, Shaolan Bi, Jinghua Zhang, Tiancheng Sun, Liu Long, Zhishuai Ge, Tanda Li, Xianfei Zhang, Xunzhou Chen, Yaguang Li, Jianzhao Zhou, Maosheng Xiang
Figure caption:  T eff–M  G  diagram of the stars in the field of LAMOST and Gaia (gray dots); the orange boundary is our selection criteria according to Section 2.
Paper: Relations of Rotation and Chromospheric Activity to Stellar Age for FGK Dwarfs from Kepler and LAMOST
Authors: Lifei Ye, Shaolan Bi, Jinghua Zhang, Tiancheng Sun, Liu Long, Zhishuai Ge, Tanda Li, Xianfei Zhang, Xunzhou Chen, Yaguang Li, Jianzhao Zhou, Maosheng Xiang
Figure caption: Likelihood distributions of mass and age in a logarithmic scale for the example star, which is the common target of our sample and asteroseismic sample. The vertical red solid line represents the most probable value, while the black dashed lines indicate the probability values at the 0.16 and 0.84 quantities.
Paper: Relations of Rotation and Chromospheric Activity to Stellar Age for FGK Dwarfs from Kepler and LAMOST
Authors: Lifei Ye, Shaolan Bi, Jinghua Zhang, Tiancheng Sun, Liu Long, Zhishuai Ge, Tanda Li, Xianfei Zhang, Xunzhou Chen, Yaguang Li, Jianzhao Zhou, Maosheng Xiang
Figure caption: Age uncertainty of stars in different effective temperature intervals as labeled in the legend.
Paper: A Study of Broad Emission Line and Doppler Factor Estimation for Fermi Blazars
Authors: Guohai Chen, Zepeng Zheng, Xiangtao Zeng, Lixia Zhang, Hubing Xiao, Xiang Liu, Lang Cui, Junhui Fan
Figure caption: (a) The redshift distribution of sources, where the black histogram represents the whole sample, the orange–red histogram represents BL Lacs, and the blue histogram represents FSRQs. (b) The Doppler factor distribution. (c) The logarithm of BLR luminosity distributions for 278 blazars (black histogram). The orange–red histogram is for BL Lacs, and the blue histogram is for FSRQs. (d) The correlation between the synchrotron peak frequency (log(ν  p /Hz)) and the BLR luminosity (logL BLR), where triangles stand for BL Lacs, circles for FSRQs, and stars for changing-look blazar candidates. The dotted line is a dividing line, and other straight lines correspond to the best-fitting results, the solid line to the whole sample (ALL), the dashed line to BL Lacs, and the dashed–dotted line to FSRQs.
Paper: Relations of Rotation and Chromospheric Activity to Stellar Age for FGK Dwarfs from Kepler and LAMOST
Authors: Lifei Ye, Shaolan Bi, Jinghua Zhang, Tiancheng Sun, Liu Long, Zhishuai Ge, Tanda Li, Xianfei Zhang, Xunzhou Chen, Yaguang Li, Jianzhao Zhou, Maosheng Xiang
Figure caption: Consistent with Figure 5, except that the y-axis is the chromospheric activity indices, and solar ﹩{R}_{\mathrm{HK}}^{+}\sim (1.209\mbox{--}1.600)\ast {10}^{-5}﹩.
Paper: A Study of Broad Emission Line and Doppler Factor Estimation for Fermi Blazars
Authors: Guohai Chen, Zepeng Zheng, Xiangtao Zeng, Lixia Zhang, Hubing Xiao, Xiang Liu, Lang Cui, Junhui Fan
Figure caption: The optical spectra of B3 0920+416 or 4FGL J0923.5+4125 (left) and 4C +25.01 or 4FGL J0018.8+2611 (right) modeled with QSOFITMORE. The spectral data are shown with the black line. Red and green lines represent broad and narrow components of the emission line, respectively, and the modeled continuum is plotted with the orange line. The blue line is the sum of all the components. Horizontal gray dashes at the top of the plots denote the line-free wavelength regions selected to model the continuum emission. The data are adopted from SDSS-DR16 for B3 0920+416 and LAMOST-DR8 for 4C +25.01.
Paper: A Study of Broad Emission Line and Doppler Factor Estimation for Fermi Blazars
Authors: Guohai Chen, Zepeng Zheng, Xiangtao Zeng, Lixia Zhang, Hubing Xiao, Xiang Liu, Lang Cui, Junhui Fan
Figure caption: The left panel shows the γ-ray luminosity vs. the broad-line luminosity, and the right panel shows the γ-ray luminosity vs. the Doppler factor, where triangles stand for BL Lacs and circles for FSRQs. The straight lines correspond to the best-fitting results: the solid line to the whole sample (ALL), the dashed line to BL Lacs, and the dashed–dotted line to FSRQs.
Paper: A Study of Broad Emission Line and Doppler Factor Estimation for Fermi Blazars
Authors: Guohai Chen, Zepeng Zheng, Xiangtao Zeng, Lixia Zhang, Hubing Xiao, Xiang Liu, Lang Cui, Junhui Fan
Figure caption: The γ-ray luminosity vs. the broad-line luminosity, where triangles stand for BL Lacs, circles for FSRQs, and squares for 88 blazars with redshifts from SDSS spectra. The straight lines correspond to the best-fitting results: the dashed line to the 88 blazars, the solid line to the 891 sources excluding the 88 blazars with redshifts from SDSS spectra (979 – 88).
Paper: A Study of Broad Emission Line and Doppler Factor Estimation for Fermi Blazars
Authors: Guohai Chen, Zepeng Zheng, Xiangtao Zeng, Lixia Zhang, Hubing Xiao, Xiang Liu, Lang Cui, Junhui Fan
Figure caption: (a) The broad-line luminosities for blazars with different synchrotron peak; the red line is for the high synchrotron peak (HSP) blazars, the yellow line is for the intermediate synchrotron peak (ISP) blazars, and the blue line is for the low synchrotron peak (LSP) blazars. (b) The broad-line luminosities for BL Lacs with different synchrotron peak; the red line is for the high synchrotron peak BL Lacs (HBLs), the yellow line is for the intermediate synchrotron peak BL Lacs (IBLs), and the blue line is for the low synchrotron peak BL Lacs (LBLs).
Paper: Relations of Rotation and Chromospheric Activity to Stellar Age for FGK Dwarfs from Kepler and LAMOST
Authors: Lifei Ye, Shaolan Bi, Jinghua Zhang, Tiancheng Sun, Liu Long, Zhishuai Ge, Tanda Li, Xianfei Zhang, Xunzhou Chen, Yaguang Li, Jianzhao Zhou, Maosheng Xiang
Figure caption: The sub-bins of Figure 6(a): panel (a) shows the distribution of stars with 1.4–1.2 M ⊙, while panel (b) displays the distribution of stars with 1.2–1.1 M ⊙. The fitting results are shown as text and solid lines.
Paper: Image Synthesis for Solar Flare Prediction
Authors: Elad Amar, Ohad Ben-Shahar
Figure caption: An illustration for SMOTE process and synthetic oversampled samples on the interpolation segment. The leftmost image shows HARP 4781 on 2014 November 6 at 17:36:00 TAI and its corresponding GMM model as confidence ellipses. The rightmost image shows HARP 377 on 2011 February 15 at 01:36:00 TA and its GMM representation (from Figure 1). The middle images illustrate synthetic oversampled GMM points sampled on the interpolation segment between the GMM representations of the two HARPs. The position of the samples along the segment represents how the properties of the two HARPs are weighted in the interpolation, including the sunspots’ location, orientation, and size. Note that while HARPs depicted here have similar image sizes, this is not necessary for the SMOTE process, as normalization by HARP size is instrinsic to the process.
Paper: Image Synthesis for Solar Flare Prediction
Authors: Elad Amar, Ohad Ben-Shahar
Figure caption: Effects of synthetic images on CNN attention: adding synthetic images to the training process directs the model’s attention toward the sunspots next to the PIL. (a) Example of HARP 4698 on 2014 October 26 at 02:24:00 TAI (left), and the heatmaps generated with 0, 300, and 1000 synthetic images added to the training process. (b) The extent of overlap between Grad-CAM heatmaps and PILs was quantified in terms of intensity, represented as the percentage of PILs contained within the heatmaps. (c) The maximum value of the element-wise product between Grad-CAM output and the convolution output may indicate the correlation between high gradients in the AR and the CNN attention. (d) The extent of overlap between Grad-CAM heatmaps and the absolute total magnetic flux of sunspots near the PIL. Panels (b), (c), and (d) exhibit a similar trend: an initial increase followed by a decrease as more synthetic images are added.
Paper: Image Synthesis for Solar Flare Prediction
Authors: Elad Amar, Ohad Ben-Shahar
Figure caption: Steps in the magnetogram’s GMM representation process. (a) The initial image, HARP 377 on 2011 February 15 at 01:36:00 TAI. (b) Segmentation of the sunspots. (c) Result of fitting a GMM to the segments in the segmentation result and plotting the corresponding confidence ellipses. Here N = 9 and K = 3.
Paper: Image Synthesis for Solar Flare Prediction
Authors: Elad Amar, Ohad Ben-Shahar
Figure caption: Generation of synthetic images from the label image. (a) The label image resulting from the PSO process (see Figure 2) and given to the image synthesis processes. (b) Output of the Pix2Pix model. (c) Output of the diffusion model.
Paper: Image Synthesis for Solar Flare Prediction
Authors: Elad Amar, Ohad Ben-Shahar
Figure caption: Effects of synthetic images on CNN attention: examples of nonflaring magnetograms and their Grad-CAM outputs, with an emphasis on the negative prediction. The regions with the largest contribution to the output are the quiet-Sun regions.
Paper: Image Synthesis for Solar Flare Prediction
Authors: Elad Amar, Ohad Ben-Shahar
Figure caption: Evaluation of the prediction model varies depending on the number of synthetic images used during the training process. (a) The recall increases when adding more synthetic images to the training. (b) The precision improves with a small addition of synthetic images.
Paper: Image Synthesis for Solar Flare Prediction
Authors: Elad Amar, Ohad Ben-Shahar
Figure caption: Evaluation of the prediction model with optimization of the GMM parameters using PSO. (a) The recall increases when adding more synthetic images to the training until saturated. (b) The precision improves with the incorporation of a small number of synthetic images but decreases as more synthetic images are introduced.
Paper: Modeling and Calibration of Gaia, Hipparcos, and Tycho-2 Astrometric Data for the Detection of Dark Companions
Authors: Fabo Feng, Yicheng Rui, Yifan Xuan, Hugh Jones
Figure caption: Distribution of calibration parameters for GDR2 and G1P5 based on 10,000 draws of 100 samples from various calibration sources. The parameter range is divided into 50 bins. The red dashed lines represent the modes of the distributions. The upper panels show the Pearson correlation value (Corr) and its significance indicated by the number of star symbols. The p-values of less than 0.1, 0.05, 0.01, and 0.001 are indicated by “.,” “*,” “**,” and “***,” respectively. No symbol is shown if the p-value is higher than 0.1.
Paper: Modeling and Calibration of Gaia, Hipparcos, and Tycho-2 Astrometric Data for the Detection of Dark Companions
Authors: Fabo Feng, Yicheng Rui, Yifan Xuan, Hugh Jones
Figure caption: Comparison of differential calibration parameters determined in this work and in literature. The left panel shows the values of β GDR23 while the right panel shows  β   d  of HIP1 and HIP2 relative to GDR3. Because Brandt (2018) determines the calibration parameters for a hybrid of HIP1 and HIP2, the same B18 values are presented for both HIP1 and HIP2.
Paper: Modeling and Calibration of Gaia, Hipparcos, and Tycho-2 Astrometric Data for the Detection of Dark Companions
Authors: Fabo Feng, Yicheng Rui, Yifan Xuan, Hugh Jones
Figure caption: Distribution of lnBF over the error inflation and jitter of GDR2 (first row), GDR3 (second row), G1P2 (third row), and G1P5 (fourth row) based on analyses of various calibration sources. The color encodes the value of lnBF of the calibration model relative to the model without calibration. The panels in the first and second rows are zoomed-in versions of distributions over a bigger grid with 1 ≤ a ≤ 2 and 0 ≤ b ≤ 2, shown in the lower panels.
Paper: Modeling the Morphology of Fast Radio Bursts and Radio Pulsars with fitburst 
Authors: E. Fonseca, Z. Pleunis, D. Breitman, K. R. Sand, B. Kharel, P. J. Boyle, C. Brar, U. Giri, V. M. Kaspi, K. W. Masui, B. W. Meyers, C. Patel, P. Scholz, K. Smith
Figure caption: Left: best-fit measurements of fitburst parameters derived from data simulated with simpulse, using an initial guess described in Section 3.2. The statistical uncertainty for each point corresponds to the 68.3% confidence interval computed by fitburst, and black dashed lines correspond to simpulse input values. Right: distributions of uncertainty-weighted deviations of τ  r  from the mean value derived from “high-S/N” (∼200) and “low-S/N” (∼20) simulated bursts, as well as one-component (N = 1) and two-component (N = 2) bursts, each consisting of 100 simulations. All sets of simulations yield estimates of uncertainty-weighted τ  r  deviates that largely reflect the normal distribution of the simulation noise (black line).
Paper: Modeling the Morphology of Fast Radio Bursts and Radio Pulsars with fitburst 
Authors: E. Fonseca, Z. Pleunis, D. Breitman, K. R. Sand, B. Kharel, P. J. Boyle, C. Brar, U. Giri, V. M. Kaspi, K. W. Masui, B. W. Meyers, C. Patel, P. Scholz, K. Smith
Figure caption: Calculation of dispersion smearing in fitburst models. By default, M  kn  will inherit the original time and frequency resolution of D  kn ; this resolution will yield a model for unresolved, highly dispersed profiles as shown in red in the topmost time series. To account for smearing within each channel, fitburst will first produce a high-resolution version of each channelized time series, upsampled along both axes by user-specified amounts. fitburst will then evaluate the model across the upsampled grid, where intrachannel dispersion can be better resolved. (The above figure shows a dispersed model of the original time series in blue shading that is obtained after upsampling in frequency by a factor of 8, and in time by a factor of 4.) The high-resolution model is then downsampled to the original resolution to form a dispersion-smeared pulse, where the signal of a dispersed, intrinsically low-width pulse now appears in more than one time sample.
Paper: Modeling the Morphology of Fast Radio Bursts and Radio Pulsars with fitburst 
Authors: E. Fonseca, Z. Pleunis, D. Breitman, K. R. Sand, B. Kharel, P. J. Boyle, C. Brar, U. Giri, V. M. Kaspi, K. W. Masui, B. W. Meyers, C. Patel, P. Scholz, K. Smith
Figure caption: The CHIME/FRB detections of FRBs 20220502B (left) and 20200928B (right), with marginalized time series and SEDs shown in the outer panels for each FRB; the orange lines correspond to the best-fit M  kn  projected onto each axis. The best-fit models for both FRBs were optimized against raw CHIME/FRB data, though the products shown here are downsampled along the frequency axis to 256 channels.
Paper: Modeling and Calibration of Gaia, Hipparcos, and Tycho-2 Astrometric Data for the Detection of Dark Companions
Authors: Fabo Feng, Yicheng Rui, Yifan Xuan, Hugh Jones
Figure caption: Similar to Figure 1, but for TYC (first row), HIP1 (second row), and HIP2 (third row).
Paper: Modeling and Calibration of Gaia, Hipparcos, and Tycho-2 Astrometric Data for the Detection of Dark Companions
Authors: Fabo Feng, Yicheng Rui, Yifan Xuan, Hugh Jones
Figure caption: Similar to Figure 2, but for the distribution of optimized calibration parameters for HIP2 (left) and HIP1 (right) based on 10,000 draws of 100 samples from various calibration sources.
Paper: Modeling and Calibration of Gaia, Hipparcos, and Tycho-2 Astrometric Data for the Detection of Dark Companions
Authors: Fabo Feng, Yicheng Rui, Yifan Xuan, Hugh Jones
Figure caption: Similar to Figure 2, but for the distribution of optimized calibration parameters for G1P2 (left) and TYC (right) based on 10,000 draws of 100 samples from various calibration sources.
Paper: Modeling and Calibration of Gaia, Hipparcos, and Tycho-2 Astrometric Data for the Detection of Dark Companions
Authors: Fabo Feng, Yicheng Rui, Yifan Xuan, Hugh Jones
Figure caption: Dependence of differential calibration parameters ( β  GDR23) on G magnitude (top) and BP − RP color (bottom) for the SS (left) and NST (right) calibration sources. Different colors represent different parameters.
Paper: Modeling and Calibration of Gaia, Hipparcos, and Tycho-2 Astrometric Data for the Detection of Dark Companions
Authors: Fabo Feng, Yicheng Rui, Yifan Xuan, Hugh Jones
Figure caption: Dependence of differential calibration parameters ( β  GDR23) on GDR3 RUWE for the SS calibration sources. Different colors represent different parameters.