adsabs/AstroCLIMB huggingface dataset:
Paper: Not-quite-transcendental Functions for Logarithmic Interpolation of Tabulated Data
Authors: Peter C. Hammond, Jacob M. Fields, Jonah M. Miller, Brandon L. Barker
Figure caption: Interpolation of the SFHo equation of state (A. W. Steiner et al. 2013) as tabulated in the Stellar Collapse database (E. O’Connor & C. D. Ott 2010). Left panel is the true table, middle is first-order NQT, and right is second order. Left panel shows pressure vs. density and temperature at fixed Ye = 0.1. Middle and right panels show relative error compared to the true-log interpolant.
Paper: Not-quite-transcendental Functions for Logarithmic Interpolation of Tabulated Data
Authors: Peter C. Hammond, Jacob M. Fields, Jonah M. Miller, Brandon L. Barker
Figure caption: Structure of a big-endian double-precision floating-point number under the IEEE754 standard.
Paper: Not-quite-transcendental Functions for Logarithmic Interpolation of Tabulated Data
Authors: Peter C. Hammond, Jacob M. Fields, Jonah M. Miller, Brandon L. Barker
Figure caption: Density slice of a three-dimensional neutron star simulated with AthenaK with logarithmic interpolation on the left and second-order NQT on the right.
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: Same as Figure 6, but for the correlation of the magnitude offsets between the standards and USS photometry after subtraction of zero-points for tile_ID=SHORTS-STRIPE82_0090 for each of the two bands. For each panel, the correlation coefficients are marked in the bottom-right corners. The linear fitting lines are shown as light-green lines, and the slopes of the lines are marked in the top-left corners. The black dashed lines denote y = x in each panel. A histogram of the distribution of the slopes is shown in the top-right corner.
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: Correlation plots of the zero-point offsets between the BEST standards and USS photometry for each of the two bands. For each panel, the correlation coefficient is displayed in the bottom-right corner only when it exceeds 0.7. The black dashed lines denote y = x in each panel. The red lines represent the results of linear fitting; the slopes are also marked in red.
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: Magnitude offsets between the XPSP and the SCR standard stars, as functions of Gaia G, GBP − GRP, and E(GBP − GRP), for all 12 bands. The colors represent the density of points. For each panel, the zero residuals and median values of the magnitude offsets are denoted by black dashed lines and red curves, respectively. The red dashed curves in the leftmost panels represent the standard deviations of the points, estimated using Gaussian fitting with a running width of 0.5 mag and a running step of 0.2 mag.
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: From top to bottom, the first 12 rows show the spatial variations of the difference between the XPSP standards and USS zero-points in each tile. The bottom row shows the distributions of the listed numbers of each tile in the r band. To clearly illustrate the spatial structure, the zero-point differences in the J0410, J0430, g, J0515, r, J0660, i, J0861, and z bands are multiplied by four. The labels are marked in each panel, and a color bar is shown on top.
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: Variations of the magnitude offsets, as a function of USS magnitude, for all 12 bands. For each panel, the colors represent the logarithm of the number density, calculated using Gaussian kernel density estimation. The red and gray lines denote the median values and zero levels of the magnitude offsets, respectively.
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: Same as Figure 4, but for magnitude offsets between the XPSP standards and the USS photometry after subtraction of the zero-points for each tile. The bands are marked on the right, and a color bar is shown on top. The tire-track-like structures observed in the figure, extending roughly from R.A. = −30° to 0 and related to Gaia’s scanning law, are mainly caused by the spatial nonuniformity of the numbers of the Gaia XP spectra. Similar structures are also observed in the bottom-right panel of Figure 4 in K. Xiao et al. (2023b).
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: Histograms of the differences in zero-points between the XPSP standards and USS photometry. The bands are marked in the top-left corner of each panel. The Gaussian fitting results are plotted as blue lines. The standard deviations when using Gaussian fitting are marked in each panel in red.
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: Same as Figure 2, but for GBP − GRP color. For the u, J0378, and g bands, the two color cuts are denoted by black dashed lines.
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: H I column density vs. 1.4 GHz flux density of radio sources for both the H I absorption systems we detected and those reported in the literature. Higher- (z > 0.5) and lower-redshift (z ≤ 0.5) samples are represented by filled points and open points, respectively. Dashed and dotted–dashed lines represent median values for intervening (green diamond) and associated (red star) systems detected in this work, respectively.
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: Redshift vs. 1.4 GHz flux density of radio sources for the H I absorption systems (upper) and H I column density (lower). The black dashed line represents the redshift threshold of 0.5, which we used to distinguish between the higher- and lower-redshift samples presented in the text.
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: Same as Figure 33, but for the H I column density vs. the SDSS g − r color. The black dashed line represents a g − r value of 0.7.
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: The WISE color–color diagram showing the locations of interesting classes of objects, overplotted with samples from our work and the literature. Higher- (z > 0.5) and lower-redshift (z ≤ 0.5) samples are represented by filled points and open points, respectively.
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: The H I column density vs. FWHM for both our sample and the sample from the literature. Higher- (z > 0.5) and lower-redshift (z ≤ 0.5) samples are represented by filled points and open points, respectively. The green and red dashed lines indicate the median values for the intervening and associated systems detected in this study.
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: Same as Figure 33, but for the minor-to-major axis ratio (left panel), peak-to-integrated flux ratio (center panel), and peak-to-ring flux ratio (right panel) vs. H I column density.
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: Left panel: the same as Figure 3, but for NVSS J225900+274356. Middle-left panel: radio map from VLASS centered at NVSS J225900+274356. Middle-right panel: W1 band infrared map of WISEA J225900.29+274356.9. from WISE. Right panel: SDSS optical map of the optical counterpart of WISEA J225900.29+274356.9.
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: Left panel: the same as Figure 2, but for NVSS J115948+582020. Middle-left panel: radio map from VLASS centered at NVSS J115948+582020. Middle-right panel: WISE W3 band infrared map centered at NVSS J115948+582020. Right panel: SDSS optical map centered at NVSS J115948+582020.
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: Left panel: the same as Figure 2, but for NGC 6150. Middle-left panel: radio map from VLASS centered at NGC 6150. Middle-right panel: W2 band infrared map centered at NGC 6150. Right panel: SDSS optical map of the SDSS J162549.96+402919.3.
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: Scatter matrix plot for the raw data showing the outliers determined by the M-estimator in cyan and the rest of the data in red.
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: Weight assigned to each feature by LASSO for zt = 3.5 with the raw data set without the M-estimator (top left), the raw data set with the M-estimator (top right), MICE-imputed data set (bottom left), and SMOTE-balanced data set (bottom right). Features with weights >2% are selected as the best features for the classification.
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: The distribution of missing data in our sample. Red boxes highlight GRBs with missing data points, while blue boxes indicate GRBs with complete data for a given variable, as noted on the top axis. The bottom axis enumerates the number of missing variables according to the GRB number shown on the left axis. The left axis represents the count of observations with missing data for specific features. For instance, there are 184 GRBs with complete data, 21 GRBs missing only βerr values, 15 GRBs missing ﹩\mathrm{log}({\rm{NH}})﹩ values, and so on. The right axis indicates the number of features with missing data for each row.
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: Flowchart detailing each pipeline step, from the initial data to the SuperLearner ensemble model. Yellow boxes indicate the sequence of steps followed in the pipeline. Orange boxes display the total number of GRBs after each step. Magenta boxes show the number of GRBs in the training, and red boxes show the number in the test sets at each process stage. Green boxes highlight the steps involved in the model construction. The gray box represents the outcome of the constructed model; both applied on the test and training sets.
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: Distributions of α, β, Γ, ﹩\mathrm{log}({\rm{NH}})﹩, ﹩\mathrm{log}({\rm{PeakFlux}})﹩, PhotonIndex, Err ﹩\mathrm{log}({T}_{90})﹩, Err ﹩\mathrm{log}({\rm{Fluence}})﹩, Err ﹩\mathrm{log}({\rm{PeakFlux}})﹩, PhotonIndexErr, Err α, and Err β are shown in panels (a)–(l) with MICE-imputed data in magenta and the original data in blue. The plots also show the p-value from the K-S test.
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: Scatter matrix plot for the MICE-imputed data showing the MICE-imputed data in red and the original data in cyan.
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: Distribution of weights assigned by the M-estimator to each GRB on the raw data. The red vertical line shows the cutoff line of 0.65 for the outliers. GRBs below this cutoff line are considered outliers.
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Time evolution of integrated current density for different numerical schemes at various mesh resolutions. Each panel corresponds to a different mesh size, and all schemes are compared within each panel. The differences between schemes reflect their varying abilities to resolve sharp magnetic features.
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Evolution of system energetics ΔEk, ΔEm, ΔEg, and ΔEth across multiple resolutions (left to right: 3002 to 24002). TENO-LAD exhibits superior performance with minimal artificial heating at coarse resolutions (3002 and 6002), while IMWENO-P achieves better energy conservation at finer resolutions (12002 and 24002).
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Comparison of the total energy evolution (ΔEtot) at 24002 resolution for three different numerical schemes (WENO-JS, TENO-LAD, and IMWENO-P). While open boundary conditions predict a natural decrease in total energy as the magnetic loop rises, only the IMWENO-P scheme reproduces this expected behavior, highlighting its superior physical fidelity.
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Time evolution of ϵglobal for different numerical schemes at various mesh resolutions. Each panel corresponds to a different mesh size, and all schemes are compared within each panel. The error metric accounts for both the magnitude of divergence errors and the magnetic field strength across the entire computational domain. Higher values indicate greater deviation from the ∇ · B = 0 condition.
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Spatial distribution of ΔL∥ ∇ · B∥ at t/τ0 = 51 for WENO-JS and TENO-LAD schemes at the finest resolution (24002). The color scale indicates the local magnitude of divergence errors. Both schemes exhibit increased divergence errors near current sheets, where rapid changes in field direction occur.
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Computational domain showing ROIs R1–R4 (colored in red, blue, green, and orange, respectively) for mass and energy conservation analysis. The purple arrow indicates the location of a magnetic field valley.
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Analysis of magnetic field divergence errors ϵ1 as a function of the damping parameter αp at different mesh resolutions ﹩\left(30{0}^{2},60{0}^{2},\,\rm{and}\,120{0}^{2}\right)﹩. WENO-family schemes show consistent optimal performance at αp ≈ 0.2 across all resolutions, while TENO-LAD exhibits resolution-dependent behavior. Lower values in the plots indicate better divergence control.
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Comparison of key physical quantities at 6002 resolution using different numerical schemes. All quantities are evaluated at ﹩x/{H}_{0}=\left({X}_{\,\rm{max}\,}/2\right)/{H}_{0}﹩, corresponding to the midpoint of the rising loop. The TENO-LAD scheme still shows oscillations in the rising velocity (d) and density profiles (p), indicating underresolved gradients. Other remarks are the same as in Figure 5.
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Time evolution of magnetic field lines (black curves), velocity vectors (white arrows), and density distribution (color) at t = 0, 33τ0, 43τ0, 47τ0 using IMWENO-P at 24002 resolution.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: (Top) Median and min–max range of absolute and relative shifts of the parameters obtained using PHOEBAI for a group of 10 synthetic light curves, plotted against the sample size. For each sample size, all shifts are averaged over the five ANNs trained during the cross-validation test. The same range is used for all axes. (Bottom) Median and min–max range of the posterior distribution scatter (standard deviation) for the main parameters after MCMC simulations using PHOEBAI. The median line represents the average value for all 10 synthetic systems, averaged over all five ANNs. Black dashed lines represent the theoretical fit showing that scatter is inversely proportional to the square root of the number of points.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: The relationship between the injected noise level (Gaussian, with the x-axis representing the noise σ) and the parameter shifts and scatter for one synthetic system. (Top) Parameter shift, defined as the absolute or relative difference between the true parameter value and the value determined from the model with the highest log probability, as a function of σ. The five curves correspond to the results from five different ANNs. (Bottom) Standard deviation of the posterior distribution for each parameter. A black reference line represents y = x.