adsabs/AstroCLIMB huggingface dataset:
Paper: Measuring Resolved Star Formation Histories from High-precision Color–Magnitude Diagrams with StarFormationHistories.jl
Authors: Christopher T. Garling, Nitya Kallivayalil, Kristen B. W. McQuinn, Jack T. Warfield, Mario Gennaro, Roger E. Cohen
Figure caption: Comparison between a Hess diagram template constructed via random sampling and a smooth template generated with our method from Section 2.2. Both methods use a PARSEC isochrone of age 12.6 Gyr with initial [M/H] = −2.8. The isochrone is overplotted in orange. The distance modulus and photometric error and completeness functions emulate those used in our analysis of the JWST/NIRCAM observations of WLM in Section 4. (a) CMD sampled with the methods of Appendix A. (b) Binned Hess diagram computed from the CMD. (c) Smooth template generated via our procedure described in Section 2.2. (d) Residual between (b) and (c) in units of standard deviations (i.e., the residual significance).
Paper: Measuring Resolved Star Formation Histories from High-precision Color–Magnitude Diagrams with StarFormationHistories.jl
Authors: Christopher T. Garling, Nitya Kallivayalil, Kristen B. W. McQuinn, Jack T. Warfield, Mario Gennaro, Roger E. Cohen
Figure caption: Hess diagram templates for three SSPs generated via the procedure outlined in Section 2.2. We show templates for a young, metal-rich population (left), a population of intermediate age and metallicity (center), and an old, metal-poor population (right) to highlight how our method performs for populations with different CMD morphologies. The populations are modeled at a distance modulus μ = 25 mag (d = 1 Mpc), consistent with that of the WLM dwarf irregular, which we analyze in Section 4. The photometric error models used to broaden the templates are based on our WLM analysis as well. The stars from the isochrones overplotted in orange. These templates are shown in units of expected number of stars per initial solar mass of stars formed in the population. These plots demonstrate that our method is capable of resolving a large dynamic range in expectation values while accurately tracking the isochrone through many phases of stellar evolution.
Paper: Measuring Resolved Star Formation Histories from High-precision Color–Magnitude Diagrams with StarFormationHistories.jl
Authors: Christopher T. Garling, Nitya Kallivayalil, Kristen B. W. McQuinn, Jack T. Warfield, Mario Gennaro, Roger E. Cohen
Figure caption: Left column: Simulated CMDs of stellar populations with birth stellar masses 107 M⊙ with constant SFRs at distances of 4 Mpc (top) and 10 Mpc (bottom). Other modeling assumptions (e.g., photometric error and completeness functions) are the same as those used for the experiments in Section 3. Stars are colored according to their ages. The apparent TRGB magnitude in the F090W filter at the distance of the simulated populations (absolute magnitude MF090W = −4.362 mag; G. S. Anand et al. 2024) is shown as a dashed line. Right column: Best-fit cumulative SFHs (solid blue line) and 68% credible intervals, considering only random uncertainty, derived from the simulated CMDs in the left column. The results are consistent with the intrinsic SFH (dashed orange line). As expected, the more distant population has greater uncertainties as the shallower data resolve less of the upper main sequence, giant branch, and red clump, while the horizontal branch is altogether unresolved.
Paper: Measuring Resolved Star Formation Histories from High-precision Color–Magnitude Diagrams with StarFormationHistories.jl
Authors: Christopher T. Garling, Nitya Kallivayalil, Kristen B. W. McQuinn, Jack T. Warfield, Mario Gennaro, Roger E. Cohen
Figure caption: The cumulative SFHs of the Horologium I UFD measured from the HST/ACS aperture photometry of H. Richstein et al. (2024) with our methodology (blue line) and SFERA (orange line, E. Sacchi et al. 2021). The 68% credible interval for our result, considering only random uncertainty, is shaded gray. The shaded red region shows the 68% confidence interval for the midpoint of reionization (Planck Collaboration et al. 2020). As Horologium I is ultrafaint ﹩\left({M}_{v}\approx -3.7\right)﹩, its CMD is sparsely populated, resulting in larger random uncertainties than we saw for WLM in Figure 9. Agreement is very good between our result and that of E. Sacchi et al. (2021).
Paper: Measuring Resolved Star Formation Histories from High-precision Color–Magnitude Diagrams with StarFormationHistories.jl
Authors: Christopher T. Garling, Nitya Kallivayalil, Kristen B. W. McQuinn, Jack T. Warfield, Mario Gennaro, Roger E. Cohen
Figure caption: Effects of observational systematics (distance, foreground reddening, and stellar binary fraction) on the measurement of the cumulative SFHs (left) and AMRs (right) of synthetic stellar populations.
Paper: Photometric Stellar Parameters for 195,478 Kepler Input Catalog Stars
Authors: Bowen Zhang, Yang Huang, Timothy C. Beers, Kai Xiao, Jifeng Liu, Lei Jia, Henggeng Han, Zhirui Li, Chuanjie Zheng, Yongkang Sun, Ruifeng Shi, Hongrui Gu
Figure caption: Distributions of the training-sample main-sequence stars (left panels) and giant stars (right panels) in the (U − BP)0 vs. (BP − RP)0 plane (top panels) and the [m1] vs. (BP − RP)0 plane (bottom panels), coded by LAMOST metallicity ([Fe/H]), as shown by the color bars to the right of each panel. The dashed lines represent equal-metallicity sequences ranging from + 0.5 (up) to  −1.0 (bottom panel) for dwarf stars and + 0.5 (up) to  −2.0 (bottom panel) for giant stars. These sequences are obtained by fitting second-order polynomials to stars in the metallicity range ∣[Fe/H]−[Fe/H]i∣ < 0.1, where [Fe/H]i represents the marked metallicity of these sequences. The numbers of stars in each panel are provided in the lower right corner of the panel.
Paper: Photometric Stellar Parameters for 195,478 Kepler Input Catalog Stars
Authors: Bowen Zhang, Yang Huang, Timothy C. Beers, Kai Xiao, Jifeng Liu, Lei Jia, Henggeng Han, Zhirui Li, Chuanjie Zheng, Yongkang Sun, Ruifeng Shi, Hongrui Gu
Figure caption: Top panels: comparison of photometric metallicity estimates from the KIS photometry with those from APOGEE DR17 for the full sample (left), main-sequence stars (middle), and giant stars (right). The red dashed lines are the one-to-one lines. The numbers of stars, mean offset, and dispersion are provided in the lower right corner of each panel. Bottom panels: residuals of the metallicity differences ([Fe/H]phot – [Fe/H]APOGEE), as a function of Gaia G magnitude. The red dashed line is the zero level. The golden lines represent the 1σ scatter. The color bar at the right of each panel codes the number density of stars.
Paper: Photometric Stellar Parameters for 195,478 Kepler Input Catalog Stars
Authors: Bowen Zhang, Yang Huang, Timothy C. Beers, Kai Xiao, Jifeng Liu, Lei Jia, Henggeng Han, Zhirui Li, Chuanjie Zheng, Yongkang Sun, Ruifeng Shi, Hongrui Gu
Figure caption: Depth (left panel) and median E(B − V) (right panel) of the 3D reddening map toward Kepler field, coded by the color bars shown to the right of each panel.
Paper: Photometric Stellar Parameters for 195,478 Kepler Input Catalog Stars
Authors: Bowen Zhang, Yang Huang, Timothy C. Beers, Kai Xiao, Jifeng Liu, Lei Jia, Henggeng Han, Zhirui Li, Chuanjie Zheng, Yongkang Sun, Ruifeng Shi, Hongrui Gu
Figure caption: Color–absolute magnitude diagram of the training sample defined in Section 4.1, coded by LAMOST metallicity, as shown in color bar to the right. The dashed lines represent the cuts to separate different types of stars, i.e., the main-sequence (MS) stars, binary stars, giant stars, turnoff stars, and blue stars with (BP − RP)0 < 0.40.
Paper: Photometric Stellar Parameters for 195,478 Kepler Input Catalog Stars
Authors: Bowen Zhang, Yang Huang, Timothy C. Beers, Kai Xiao, Jifeng Liu, Lei Jia, Henggeng Han, Zhirui Li, Chuanjie Zheng, Yongkang Sun, Ruifeng Shi, Hongrui Gu
Figure caption: Comparison of photometric metallicity estimates derived from KIS photometry with those having synthesized Strömgren from Gaia XP spectra. The red dashed line is the one-to-one line. The color bar at right codes the number density of stars. The numbers of stars, mean offset, and dispersion are provided in the lower right corner.
Paper: Photometric Stellar Parameters for 195,478 Kepler Input Catalog Stars
Authors: Bowen Zhang, Yang Huang, Timothy C. Beers, Kai Xiao, Jifeng Liu, Lei Jia, Henggeng Han, Zhirui Li, Chuanjie Zheng, Yongkang Sun, Ruifeng Shi, Hongrui Gu
Figure caption: Comparison of photometric [Fe/H] estimates between wide binary members. Left panel: comparison of our method based on synthetic photometric data generated from Gaia XP spectra. Right panel: our method based on KIS photometric data. The red dashed lines are the one-to-one lines. The numbers of stars, mean offset, and dispersion are provided in the upper left corner of each panel.
Paper: Photometric Stellar Parameters for 195,478 Kepler Input Catalog Stars
Authors: Bowen Zhang, Yang Huang, Timothy C. Beers, Kai Xiao, Jifeng Liu, Lei Jia, Henggeng Han, Zhirui Li, Chuanjie Zheng, Yongkang Sun, Ruifeng Shi, Hongrui Gu
Figure caption: Similar to Figure 5, but for photometric metallicity estimates from Strömgren photometry synthesized from the Gaia XP spectra.
Paper: Energetic Transients Joint Analysis System for Multi-INstrument (ETJASMIN) for GECAM. II. Search, Verification, and Classification of Bursts
Authors: Ce Cai, Yan-Qiu Zhang, Shao-Lin Xiong, Jin-Peng Zhang, Ping Wang, Shi-Jie Zheng, Shuo Xiao, Qi-Bin Yi, Yi Zhao, Hao-Xuan Guo, Sheng-Lun Xie, Wang-Chen Xue, Chao Zheng, Jia-Cong Liu, Chen-Wei Wang, Wen-Jun Tan, Yue Wang, Pei-Yi Feng, Zheng-Hang Yu, Peng Zhang, Yan-Ting Zhang, Wen-Long Zhang, Xiao-Yun Zhao, Zhen Zhang, Xiang Ma, Yue Huang, Xiao-Bo Li, Cheng-Kui Li, Ming-Yu Ge, Shu-Xu Yi, Hai-Sheng Zhao, Jin Wang, Bing Li, Li-Ming Song, Lian Tao, Shu Zhang, Shuang-Nan Zhang
Figure caption: The light curves of a particle event, observed by GECAM-B (orange lines) and GBM (blue lines). Top panel: the GRD light curve of GECAM-B. Middle panel: the CPD light curve of GECAM-B. Bottom panel: the GRD light curve of GBM.
Paper: Energetic Transients Joint Analysis System for Multi-INstrument (ETJASMIN) for GECAM. II. Search, Verification, and Classification of Bursts
Authors: Ce Cai, Yan-Qiu Zhang, Shao-Lin Xiong, Jin-Peng Zhang, Ping Wang, Shi-Jie Zheng, Shuo Xiao, Qi-Bin Yi, Yi Zhao, Hao-Xuan Guo, Sheng-Lun Xie, Wang-Chen Xue, Chao Zheng, Jia-Cong Liu, Chen-Wei Wang, Wen-Jun Tan, Yue Wang, Pei-Yi Feng, Zheng-Hang Yu, Peng Zhang, Yan-Ting Zhang, Wen-Long Zhang, Xiao-Yun Zhao, Zhen Zhang, Xiang Ma, Yue Huang, Xiao-Bo Li, Cheng-Kui Li, Ming-Yu Ge, Shu-Xu Yi, Hai-Sheng Zhao, Jin Wang, Bing Li, Li-Ming Song, Lian Tao, Shu Zhang, Shuang-Nan Zhang
Figure caption: The sky map for a particle event of GECAM-B, GECAM-C, and GBM. The different colors of shaded regions represent the Earth occultation from different instruments at the time of 2023 February 6T08:47:04.100 UTC. The blue point is the location of this particle event from the GECAM ground search pipeline (C. Cai et al. 2025).
Paper: Energetic Transients Joint Analysis System for Multi-INstrument (ETJASMIN) for GECAM. II. Search, Verification, and Classification of Bursts
Authors: Ce Cai, Yan-Qiu Zhang, Shao-Lin Xiong, Jin-Peng Zhang, Ping Wang, Shi-Jie Zheng, Shuo Xiao, Qi-Bin Yi, Yi Zhao, Hao-Xuan Guo, Sheng-Lun Xie, Wang-Chen Xue, Chao Zheng, Jia-Cong Liu, Chen-Wei Wang, Wen-Jun Tan, Yue Wang, Pei-Yi Feng, Zheng-Hang Yu, Peng Zhang, Yan-Ting Zhang, Wen-Long Zhang, Xiao-Yun Zhao, Zhen Zhang, Xiang Ma, Yue Huang, Xiao-Bo Li, Cheng-Kui Li, Ming-Yu Ge, Shu-Xu Yi, Hai-Sheng Zhao, Jin Wang, Bing Li, Li-Ming Song, Lian Tao, Shu Zhang, Shuang-Nan Zhang
Figure caption: The position for a particle event of GECAM-B (yellow point), GECAM-C (purple point), and GBM (blue point), at the time of 2023 February 6T08:47:04.100 UTC. The blue, yellow, and purple shaded regions represent the SAA regions (i.e., trapped particle regions) of GECAM-B, GECAM-C, and GBM, respectively.
Paper: Solar Cycle Prediction Using a Temporal Convolutional Network Deep-learning Model with a One-step Pattern
Authors: Cui Zhao, Kun Liu, Shangbin Yang, Jinchao Xia, Jingxia Chen, Jie Ren, Shiyuan Liu, Fangyuan He
Figure caption: Time-series prediction: (a) multistep prediction, (b) one-step prediction.
Paper: Solar Cycle Prediction Using a Temporal Convolutional Network Deep-learning Model with a One-step Pattern
Authors: Cui Zhao, Kun Liu, Shangbin Yang, Jinchao Xia, Jingxia Chen, Jie Ren, Shiyuan Liu, Fangyuan He
Figure caption: Autoregressive strategy. The model generates one output at a time and then adds it to the input sequence to generate the next output.
Paper: Solar Cycle Prediction Using a Temporal Convolutional Network Deep-learning Model with a One-step Pattern
Authors: Cui Zhao, Kun Liu, Shangbin Yang, Jinchao Xia, Jingxia Chen, Jie Ren, Shiyuan Liu, Fangyuan He
Figure caption: Prediction performance of the solar cycles from 20 to 25 by the one-step pattern.
Paper: Solar Cycle Prediction Using a Temporal Convolutional Network Deep-learning Model with a One-step Pattern
Authors: Cui Zhao, Kun Liu, Shangbin Yang, Jinchao Xia, Jingxia Chen, Jie Ren, Shiyuan Liu, Fangyuan He
Figure caption: Prediction of solar cycle 25 by the one-step pattern. (a) Forecast trend shown on multiple solar cycles. (b) Enlargement of the prediction results of solar cycle 25.
Paper: Solar Cycle Prediction Using a Temporal Convolutional Network Deep-learning Model with a One-step Pattern
Authors: Cui Zhao, Kun Liu, Shangbin Yang, Jinchao Xia, Jingxia Chen, Jie Ren, Shiyuan Liu, Fangyuan He
Figure caption: A dilated causal convolution with dilation factors d = 1, 2, 4. The receptive field is able to cover all values from the input sequence.
Paper: Solar Cycle Prediction Using a Temporal Convolutional Network Deep-learning Model with a One-step Pattern
Authors: Cui Zhao, Kun Liu, Shangbin Yang, Jinchao Xia, Jingxia Chen, Jie Ren, Shiyuan Liu, Fangyuan He
Figure caption: Sunspot number data we used, where the black line represents the training set and the blue one represents the test set.
Paper: Solar Cycle Prediction Using a Temporal Convolutional Network Deep-learning Model with a One-step Pattern
Authors: Cui Zhao, Kun Liu, Shangbin Yang, Jinchao Xia, Jingxia Chen, Jie Ren, Shiyuan Liu, Fangyuan He
Figure caption: Fitting performance of the solar cycles from 20 to 25 by the one-step pattern.
Paper: Solar Cycle Prediction Using a Temporal Convolutional Network Deep-learning Model with a One-step Pattern
Authors: Cui Zhao, Kun Liu, Shangbin Yang, Jinchao Xia, Jingxia Chen, Jie Ren, Shiyuan Liu, Fangyuan He
Figure caption: Visualization of a stack of causal convolutional layers.
Paper: Solar Cycle Prediction Using a Temporal Convolutional Network Deep-learning Model with a One-step Pattern
Authors: Cui Zhao, Kun Liu, Shangbin Yang, Jinchao Xia, Jingxia Chen, Jie Ren, Shiyuan Liu, Fangyuan He
Figure caption: Fitting performance of the solar cycles from 20 to 25 by the multistep pattern.
Paper: Solar Cycle Prediction Using a Temporal Convolutional Network Deep-learning Model with a One-step Pattern
Authors: Cui Zhao, Kun Liu, Shangbin Yang, Jinchao Xia, Jingxia Chen, Jie Ren, Shiyuan Liu, Fangyuan He
Figure caption: TCN residual block.
Paper: The Interaction of a Supernova Remnant with Background Interstellar Turbulence
Authors: Giuseppe Prete, Silvia Perri, Claudio Meringolo, Leonardo Primavera, Sergio Servidio
Figure caption: (a) Density map distribution after the application of the ring-shaped mask (overplotted in gray), located within the CD. (b) Autocorrelation function map computed on the ring-shaped region. The red solid line represents the autocorrelation length, namely the isocontour of C2 = 1/e. ℓx and ℓy are the increments along the x- and y-directions, respectively.
Paper: The Interaction of a Supernova Remnant with Background Interstellar Turbulence
Authors: Giuseppe Prete, Silvia Perri, Claudio Meringolo, Leonardo Primavera, Sergio Servidio
Figure caption: Spatial distribution of (a) number density, (b) pressure, (c) temperature, (d) and (e) x- and y-components of the velocity, and (f) the magnetic field magnitude from Run 2 at an age of 3000 yr.
Paper: The Interaction of a Supernova Remnant with Background Interstellar Turbulence
Authors: Giuseppe Prete, Silvia Perri, Claudio Meringolo, Leonardo Primavera, Sergio Servidio
Figure caption: Zoom ins of the spatial distributions of (a) the number density and (b) the magnetic field amplitude.
Paper: The Interaction of a Supernova Remnant with Background Interstellar Turbulence
Authors: Giuseppe Prete, Silvia Perri, Claudio Meringolo, Leonardo Primavera, Sergio Servidio
Figure caption: (a) Power spectral density of the total magnetic field (blue solid line). The dashed–dotted line represents the Kolmogorov scaling. (b) Initial magnetic field obtained by computing the curl of the Az potential vector.
Paper: The Interaction of a Supernova Remnant with Background Interstellar Turbulence
Authors: Giuseppe Prete, Silvia Perri, Claudio Meringolo, Leonardo Primavera, Sergio Servidio
Figure caption: (a) Time evolution of the maximum value of the magnetic field in the domain, (b) the total kinetic energy, (c) the magnetic energy, and (d) the thermal energy, all averaged over the whole simulation domain.
Paper: The Interaction of a Supernova Remnant with Background Interstellar Turbulence
Authors: Giuseppe Prete, Silvia Perri, Claudio Meringolo, Leonardo Primavera, Sergio Servidio
Figure caption: Time evolution of (a) the FS radius and (b) the FS speed in Run 2. A comparison with the trends predicted by the Sedov–Taylor phase is also given (red dashed lines).
Paper: The Interaction of a Supernova Remnant with Background Interstellar Turbulence
Authors: Giuseppe Prete, Silvia Perri, Claudio Meringolo, Leonardo Primavera, Sergio Servidio
Figure caption: (a) Power spectrum of the density (green solid line). (b) Background density map.
Paper: The Interaction of a Supernova Remnant with Background Interstellar Turbulence
Authors: Giuseppe Prete, Silvia Perri, Claudio Meringolo, Leonardo Primavera, Sergio Servidio
Figure caption: (a) Chandra brightness map with the ring-shaped mask overlapped. (b) Autocorrelation function map calculated on the ring-shaped region. The red solid line represents the autocorrelation length, namely the isocontour of C2 = 1/e. (c) Autocorrelation length as a function of angle obtained for Chandra image. (d) Spectra obtained from analysis of the Chandra surface brightness.
Paper: The Interaction of a Supernova Remnant with Background Interstellar Turbulence
Authors: Giuseppe Prete, Silvia Perri, Claudio Meringolo, Leonardo Primavera, Sergio Servidio
Figure caption: Spatial distribution of the magnetic field magnitude for Run 8, Run 2, Run 9, and Run 10 with an age of 3000 yr.
Paper: The Interaction of a Supernova Remnant with Background Interstellar Turbulence
Authors: Giuseppe Prete, Silvia Perri, Claudio Meringolo, Leonardo Primavera, Sergio Servidio
Figure caption: Spatial distribution of the number density for Run 8, Run 2, Run 9, and Run 10 with an age of 3000 yr.
Paper: The Interaction of a Supernova Remnant with Background Interstellar Turbulence
Authors: Giuseppe Prete, Silvia Perri, Claudio Meringolo, Leonardo Primavera, Sergio Servidio
Figure caption: Spatial distribution of the number density, pressure, temperature, x- and y-components of the velocity, and of the magnetic field magnitude from the two-dimensional simulation with an age of 3000 yr for the case Mej = 3 M⊙ (Run 6).
Paper: The Interaction of a Supernova Remnant with Background Interstellar Turbulence
Authors: Giuseppe Prete, Silvia Perri, Claudio Meringolo, Leonardo Primavera, Sergio Servidio
Figure caption: Spatial distribution of the number density, pressure, temperature, x- and y-components of the velocity, and of the magnetic field magnitude from the two-dimensional simulation with an age of 3000 yr for the case Mej = 5 M⊙ (Run 7).
Paper: The Interaction of a Supernova Remnant with Background Interstellar Turbulence
Authors: Giuseppe Prete, Silvia Perri, Claudio Meringolo, Leonardo Primavera, Sergio Servidio
Figure caption: Spectra obtained for Run 4, Run 5, and Run 2.
Paper: The Interaction of a Supernova Remnant with Background Interstellar Turbulence
Authors: Giuseppe Prete, Silvia Perri, Claudio Meringolo, Leonardo Primavera, Sergio Servidio
Figure caption: Spectra obtained for Run 1, Run 2, and Run 3.
Paper: Radiative Parameters of Odd-parity Levels in Sm III
Authors: Meina Liu, Huifang Li, Hongfeng Zheng, Huiting Ma, Meijia Zhang, Zhenwen Dai
Figure caption: Comparison of our log(gf) with those by E. Biémont et al. (2003) and by C. Ryder (2011). The dashed line marks the equality. The blue upward triangles denote the pure theoretical results by E. Biémont et al. (2003) using the HFR+CPOL method, the black squares denote the values derived by E. Biémont et al. (2003) through combining HFR lifetimes and experimental BFs measured by E. Biémont et al. (2003), the green downward triangles denote the values by C. Ryder (2011) at Wayne State University (WSU), through combining the theoretical or experimental lifetimes of E. Biémont et al. (2003) and the BFs of C. Ryder (2011), and the red circles denote experimental values by E. Biémont et al. (2003) derived through combining TR-LIF lifetimes and BFs by Fourier transform spectroscopy. The reader is reminded that these data are not independent of one another, as we have used the HFR+CPOL BFs of E. Biémont et al. (2003) to determine log(gf)s from our measured lifetimes, and C. Ryder (2011) used a combination of experimental and theoretical lifetimes from E. Biémont et al. (2003) to determine log(gf)s from their BFs.
Paper: Radiative Parameters of Odd-parity Levels in Sm III
Authors: Meina Liu, Huifang Li, Hongfeng Zheng, Huiting Ma, Meijia Zhang, Zhenwen Dai
Figure caption: A typical fluorescence decay curve of the Sm III level at 37,715.91 cm−1 with an exponential fit.
Paper: Physical Parameters of 12,201 ASAS-SN Contact Binaries Determined by a Neural Network
Authors: Kai Li, Li-Heng Wang
Figure caption: The left panel shows the distribution in transfer parameter β and luminosity ratio λ. Dashed lines are the expected β curves for different values of α. The right panel shows the relationship between bolometric luminosity ratio and mass ratio (q). The dashed line is from L. B. Lucy (1968b)’s relation λ = q0.92, and the solid line is the main-sequence mass–luminosity relation, λ = q4.216.
Paper: Physical Parameters of 12,201 ASAS-SN Contact Binaries Determined by a Neural Network
Authors: Kai Li, Li-Heng Wang
Figure caption: The distributions of the differences between the light maxima (Δm) and the correlations between the orbital period and ﹩\left|{\rm{\Delta }}m\right|﹩, and the temperature of the primary and ﹩\left|{\rm{\Delta }}m\right|﹩. The inset in the middle panel shows an enlargement of the range from 0.2 to 1 day, and the inset in the right panel shows an enlargement of the range from 4000 to 8000 K.
Paper: Physical Parameters of 12,201 ASAS-SN Contact Binaries Determined by a Neural Network
Authors: Kai Li, Li-Heng Wang
Figure caption: The distributions of the binaries in orbital period, T2/T1, q, i, f, and numbers with spots for the two subtype systems. The inset in the orbital-period distribution shows an enlargement of the range from 0.2 to 1 day.
Paper: Physical Parameters of 12,201 ASAS-SN Contact Binaries Determined by a Neural Network
Authors: Kai Li, Li-Heng Wang
Figure caption: Loss function of the training and test sets of Model 5. “MSE” on the y-axis is mean square error.
Paper: Physical Parameters of 12,201 ASAS-SN Contact Binaries Determined by a Neural Network
Authors: Kai Li, Li-Heng Wang
Figure caption: The parameter distributions of Model 5.
Paper: Physical Parameters of 12,201 ASAS-SN Contact Binaries Determined by a Neural Network
Authors: Kai Li, Li-Heng Wang
Figure caption: The posterior distributions of the parameters and the best fits for two systems.