adsabs/AstroCLIMB huggingface dataset:
Paper: Estimating Stellar Atmospheric Parameters and [α/Fe] for LAMOST O-M-type Stars Using a Spectral Emulator
Authors: Jun-Chao Liang, A-Li Luo, Yin-Bi Li, Xiao-Xiao Ma, Shuo Li, Shu-Guo Ma, Hai-Ling Lu, Yun-Jin Zhang, Bing Du, Xiao Kong
Figure caption: Diagram of the workflow for predicting atmospheric parameters. The dashed boxes show the training process of the spectral emulator using the MaStar library. The yellow boxes indicate mapping the stellar atmospheric parameter space to principal component space using PCA within each group of LAMOST spectra. The process from “grouping LAMOST spectra” to “χ 2 convergence” is termed the grouping optimization strategy. This strategy uses the trained spectral emulator and Bayesian optimization to find the χ 2 minimum in the principal component space, obtaining the final atmospheric physical parameters.
Paper: Estimating Stellar Atmospheric Parameters and [α/Fe] for LAMOST O-M-type Stars Using a Spectral Emulator
Authors: Jun-Chao Liang, A-Li Luo, Yin-Bi Li, Xiao-Xiao Ma, Shuo Li, Shu-Guo Ma, Hai-Ling Lu, Yun-Jin Zhang, Bing Du, Xiao Kong
Figure caption: Comparison of the FGK-type stars' parameters in the recommended catalog with APOGEE DR16. Mean and standard deviation of the parameter differences between the recommended catalog and APOGEE are also shown in the plot.
Paper: Estimating Stellar Atmospheric Parameters and [α/Fe] for LAMOST O-M-type Stars Using a Spectral Emulator
Authors: Jun-Chao Liang, A-Li Luo, Yin-Bi Li, Xiao-Xiao Ma, Shuo Li, Shu-Guo Ma, Hai-Ling Lu, Yun-Jin Zhang, Bing Du, Xiao Kong
Figure caption: Distribution of χ 2 values for different spectral types and the variation of S/N with χ 2 distribution. The spectral classifications were derived from the LAMOST spectral analysis pipeline (A.-L. Luo et al. 2015). The horizontal axis represents the χ 2 values, the left y-axis of each subplot indicates the number of stars, and the right y-axis represents the S/N. In each subplot, the histogram illustrates the distribution of χ 2 values of different spectral types, the green curve denotes the variation of the average S/N with χ 2 within each bin, and the black horizontal dashed line indicates the threshold for the S/N. The spectra with S/Ns below this threshold were not included in our recommended catalog.
Paper: Estimating Stellar Atmospheric Parameters and [α/Fe] for LAMOST O-M-type Stars Using a Spectral Emulator
Authors: Jun-Chao Liang, A-Li Luo, Yin-Bi Li, Xiao-Xiao Ma, Shuo Li, Shu-Guo Ma, Hai-Ling Lu, Yun-Jin Zhang, Bing Du, Xiao Kong
Figure caption: Comparison of the [α/Fe] in the recommended catalog with APOGEE DR16 in dM (left panel), gM (middle panel), and FGK-type (right panel) stars. Mean and standard deviation of the parameter differences between the recommended catalog and APOGEE are also shown in the plot.
Paper: Estimating Stellar Atmospheric Parameters and [α/Fe] for LAMOST O-M-type Stars Using a Spectral Emulator
Authors: Jun-Chao Liang, A-Li Luo, Yin-Bi Li, Xiao-Xiao Ma, Shuo Li, Shu-Guo Ma, Hai-Ling Lu, Yun-Jin Zhang, Bing Du, Xiao Kong
Figure caption: Comparison of the dM parameters in LASPM with APOGEE DR16. Mean and standard deviation of the parameter differences between LASPM and APOGEE are also shown in the plot.
Paper: Estimating Stellar Atmospheric Parameters and [α/Fe] for LAMOST O-M-type Stars Using a Spectral Emulator
Authors: Jun-Chao Liang, A-Li Luo, Yin-Bi Li, Xiao-Xiao Ma, Shuo Li, Shu-Guo Ma, Hai-Ling Lu, Yun-Jin Zhang, Bing Du, Xiao Kong
Figure caption: Comparison of the gM parameters in LASPM with APOGEE DR16. Mean and standard deviation of the parameter differences between LASPM and APOGEE are also shown in the plot.
Paper: Estimating Stellar Atmospheric Parameters and [α/Fe] for LAMOST O-M-type Stars Using a Spectral Emulator
Authors: Jun-Chao Liang, A-Li Luo, Yin-Bi Li, Xiao-Xiao Ma, Shuo Li, Shu-Guo Ma, Hai-Ling Lu, Yun-Jin Zhang, Bing Du, Xiao Kong
Figure caption: The T eff−log g (left panel) and [Fe/H]−[α/Fe] (right panel) diagrams for the recommended catalog. Both figures are color-coded by the stellar number density. In the right panel, the red and black curves respectively represent the relationship between the median values of [α/Fe] and [Fe/H] for giants and dwarfs. The parameter distribution is similar to that of the MaStar (Abdurro’uf et al. 2022).
Paper: Estimating Stellar Atmospheric Parameters and [α/Fe] for LAMOST O-M-type Stars Using a Spectral Emulator
Authors: Jun-Chao Liang, A-Li Luo, Yin-Bi Li, Xiao-Xiao Ma, Shuo Li, Shu-Guo Ma, Hai-Ling Lu, Yun-Jin Zhang, Bing Du, Xiao Kong
Figure caption: Comparison of the dM parameters in the recommended catalog with APOGEE DR16. Mean and standard deviation of the parameter differences between the recommended catalog and APOGEE are also shown in the plot.
Paper: Estimating Stellar Atmospheric Parameters and [α/Fe] for LAMOST O-M-type Stars Using a Spectral Emulator
Authors: Jun-Chao Liang, A-Li Luo, Yin-Bi Li, Xiao-Xiao Ma, Shuo Li, Shu-Guo Ma, Hai-Ling Lu, Yun-Jin Zhang, Bing Du, Xiao Kong
Figure caption: Comparison of the gM parameters in the recommended catalog with APOGEE DR16. Mean and standard deviation of the parameter differences between the recommended catalog and APOGEE are also shown in the plot.
Paper: Estimating Stellar Atmospheric Parameters and [α/Fe] for LAMOST O-M-type Stars Using a Spectral Emulator
Authors: Jun-Chao Liang, A-Li Luo, Yin-Bi Li, Xiao-Xiao Ma, Shuo Li, Shu-Guo Ma, Hai-Ling Lu, Yun-Jin Zhang, Bing Du, Xiao Kong
Figure caption: Comparison of the FGK-type stars' parameters in the recommended catalog with PASTEL. Mean and standard deviation of the parameter differences between the recommended catalog and PASTEL are also shown in the plot.
Paper: Long-term Simultaneous Monitoring Observations of SiO and H2O Masers toward the Mira Variable WX Serpentis
Authors: Jang-Ho Lim, Jaeheon Kim, Se-Hyung Cho, Hyosun Kim, Dong-Hwan Yoon, Seong-Min Son, Kyung-Won Suh
Figure caption: Peak velocity variations for all detected maser lines (panels (b)–(f)) relative to the OLC (panel (a)) over the period of 2009–2021. The vertical dotted lines represent the optical maximum phases. Green dashed lines indicate the V * for WX Ser. The color bar on the right side represents the range of peak velocities of masers.
Paper: Long-term Simultaneous Monitoring Observations of SiO and H2O Masers toward the Mira Variable WX Serpentis
Authors: Jang-Ho Lim, Jaeheon Kim, Se-Hyung Cho, Hyosun Kim, Dong-Hwan Yoon, Seong-Min Son, Kyung-Won Suh
Figure caption: Time series of the H2O maser peak velocities from 2003 to 2021. The observation data from 2003 to 2006 were extracted from the work of M. Shintani et al. (2008), while the data from 2009 to 2021 correspond to our own monitoring observations. The dashed green line represents the V *, while the dashed–dotted red lines indicate inclinations of each component determined from the linear regression fitting method. A color scale is used to represent the magnitude of peak velocity. In the inserted equation, the unit of x is days, while y represents V peak.
Paper: Long-term Simultaneous Monitoring Observations of SiO and H2O Masers toward the Mira Variable WX Serpentis
Authors: Jang-Ho Lim, Jaeheon Kim, Se-Hyung Cho, Hyosun Kim, Dong-Hwan Yoon, Seong-Min Son, Kyung-Won Suh
Figure caption: Temporal variations of the FWZIs of SiO and H2O maser lines. The vertical dashed lines indicate the optical maximum phases. The symbols used are presented in the legend inside the plot. Each line represents the result of linear fitting to the FWZI of each maser. The red solid line represents the linear fitting line for the FWZI of the H2O maser, derived from the values of the B4 and R2 components (see text).
Paper: Long-term Simultaneous Monitoring Observations of SiO and H2O Masers toward the Mira Variable WX Serpentis
Authors: Jang-Ho Lim, Jaeheon Kim, Se-Hyung Cho, Hyosun Kim, Dong-Hwan Yoon, Seong-Min Son, Kyung-Won Suh
Figure caption: Mean velocity variation of all detected maser lines relative to the V * (panels (b)–(f)) and the OLC (panel (a)) over the period of 2009–2021. The vertical dotted lines indicate the optical maximum phases. Green dashed lines indicate the V * for WX Ser.
Paper: Long-term Simultaneous Monitoring Observations of SiO and H2O Masers toward the Mira Variable WX Serpentis
Authors: Jang-Ho Lim, Jaeheon Kim, Se-Hyung Cho, Hyosun Kim, Dong-Hwan Yoon, Seong-Min Son, Kyung-Won Suh
Figure caption: Integrated flux density (I.F.) ratios of the H2O, SiO42, SiO86, and SiO129 masers to the SiO43 maser according to the normalized optical phase.
Paper: The Thermal Emission in Short Gamma-Ray Bursts with Extended Emission Observed by Fermi/GBM
Authors: Xue-Zhao Chang, Hou-Jun Lü, Xing Yang, Jia-Ming Chen, En-Wei Liang
Figure caption: Similar to Figure 3, but for GRB 170626A.
Paper: Impacts of Data Preprocessing and Sampling Techniques on Solar Flare Prediction from Multivariate Time Series Data of Photospheric Magnetic Field Parameters
Authors: MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: In-depth comparison of max TSS and max recall across different sampling techniques and classifiers: This figure illustrates the performance of various sampling methods, including exclusive oversampling techniques and combinations of oversampling and undersampling techniques. The efficacy of these methods is evaluated using four distinct classifiers: SVM, MLP, k-NN, and RF. Each shape in the plot represents the result of classification using four train–test combinations, resulting in four values. Generally, each shape in the plot shows the result of one of the three categories on one of the four classifiers across four train–test combinations, totaling four experiments. The size of each shape indicates the mean TSS, while the placement of the shape is determined by the max TSS and max recall.
Paper: Impacts of Data Preprocessing and Sampling Techniques on Solar Flare Prediction from Multivariate Time Series Data of Photospheric Magnetic Field Parameters
Authors: MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: This analysis evaluates the performance of oversampling techniques vs. combined oversampling and undersampling methods across eight classifiers and four unique training and testing combinations. The top box displays the results of four oversampling techniques applied to the eight classifiers, each with four different train–test combinations, resulting in a total of 128 experiments. The bottom box presents the same set of experiments but uses four combined oversampling and undersampling techniques. In the plot, diamond-shaped symbols indicate outliers.
Paper: Impacts of Data Preprocessing and Sampling Techniques on Solar Flare Prediction from Multivariate Time Series Data of Photospheric Magnetic Field Parameters
Authors: MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: This figure effectively showcases the preprocessing pipelines, detailing the specific steps and methods employed for both training (panel (A)) and test sets (panel (B)). To ensure an unbiased comparison of the sampling results, it is crucial that the test sets remain unaltered, with no samples being either removed or added.
Paper: Impacts of Data Preprocessing and Sampling Techniques on Solar Flare Prediction from Multivariate Time Series Data of Photospheric Magnetic Field Parameters
Authors: MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: Comparative analysis of mean and max TSS across various sampling techniques and classifiers: This figure illustrates the performance of different sampling methods, including exclusive oversampling techniques (SMOTE, ADASYN, GNI, TimeGAN) and a combination of oversampling and undersampling approaches (RUS and TL with one of the oversampling methods). The efficacy of these techniques is evaluated using four distinct classifiers: SVM, MLP, k-NN, and RF. For each bar, the first value indicates the mean TSS, and the second value indicates the max TSS of four train–test combinations.
Paper: Impacts of Data Preprocessing and Sampling Techniques on Solar Flare Prediction from Multivariate Time Series Data of Photospheric Magnetic Field Parameters
Authors: MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: The comparative analysis of max TSS and max recall across different imputation (I) and normalization (N) techniques is illustrated. The efficacy of these algorithms is assessed using SVM, MLP, k-NN, and RF classifiers. In the plot, each shape represents the result of classification using the four train–test combinations with one of the mentioned classifiers. The size of each shape indicates the mean TSS, while the position of the shape is determined by the max TSS and max recall values.
Paper: Impacts of Data Preprocessing and Sampling Techniques on Solar Flare Prediction from Multivariate Time Series Data of Photospheric Magnetic Field Parameters
Authors: MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: This figure presents a comparison of the mean and max TSS scores achieved by various NDBSR methods when applied to different classifiers. These classifiers include MLP, RF, SVM, and k-NN. For each bar, the first value indicates the mean TSS, and the second value indicates the max TSS of four train–test combinations.
Paper: Impacts of Data Preprocessing and Sampling Techniques on Solar Flare Prediction from Multivariate Time Series Data of Photospheric Magnetic Field Parameters
Authors: MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: t-SNE visualizations demonstrating the distributional alignment of original and synthetic data samples for each oversampling technique across data set partitions. These visualizations highlight the efficacy of each oversampling method in replicating the comprehensive distribution characteristics of the original data samples.
Paper: Impacts of Data Preprocessing and Sampling Techniques on Solar Flare Prediction from Multivariate Time Series Data of Photospheric Magnetic Field Parameters
Authors: MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: Assessing TSS scores of the eight sampling techniques across our eight classifiers and four unique training and test combinations. Each box shows the result of a specific sampling technique on eight classifiers and four train–test combinations, totaling 32 experiments. In the plot, diamond-shaped symbols indicate outliers.
Paper: Impacts of Data Preprocessing and Sampling Techniques on Solar Flare Prediction from Multivariate Time Series Data of Photospheric Magnetic Field Parameters
Authors: MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Figure caption: Comparative analysis of the mean and max TSS values across nine combinations of imputation (I) and normalization (N) techniques. Efficacy evaluated via four classifiers: SVM, MLP, k-NN, and RF. For each bar, the first value indicates the mean TSS, and the second value indicates the max TSS of four train–test combinations.
Paper: The Thermal Emission in Short Gamma-Ray Bursts with Extended Emission Observed by Fermi/GBM
Authors: Xue-Zhao Chang, Hou-Jun Lü, Xing Yang, Jia-Ming Chen, En-Wei Liang
Figure caption: Left: HR vs. T 90 diagram of our sample and other long- and short-GRBs. The solid and dashed lines are the dividing lines of long and short GRBs from the BATSE and Fermi (A. von Kienlin et al. 2020) catalogs, respectively. Right: the f eff vs. T 90 diagram. The gray vertical line is the division line at 2 s. The data of long- and short-GRBs are taken from H.-J. Lü et al. (2014). GRB 060614 and our SGRBs with EE samples are highlighted by the purple square and the red star, respectively.
Paper: The Thermal Emission in Short Gamma-Ray Bursts with Extended Emission Observed by Fermi/GBM
Authors: Xue-Zhao Chang, Hou-Jun Lü, Xing Yang, Jia-Ming Chen, En-Wei Liang
Figure caption: Temporal evolution of α and Γ (a), kT (b), F BB (c), and F BB/F tot (d) of GRB 081215. The dashed line is the limiting value of α = −2/3.
Paper: The Thermal Emission in Short Gamma-Ray Bursts with Extended Emission Observed by Fermi/GBM
Authors: Xue-Zhao Chang, Hou-Jun Lü, Xing Yang, Jia-Ming Chen, En-Wei Liang
Figure caption: The distributions of α and Gaussian fitting for ME (black solid line), EE (red dashed line), and whole burst (blue dotted line). The gray dashed line is the death line of synchrotron emission.
Paper: The Thermal Emission in Short Gamma-Ray Bursts with Extended Emission Observed by Fermi/GBM
Authors: Xue-Zhao Chang, Hou-Jun Lü, Xing Yang, Jia-Ming Chen, En-Wei Liang
Figure caption: Similar to Figure 3, but for GRB 100829A.
Paper: The Thermal Emission in Short Gamma-Ray Bursts with Extended Emission Observed by Fermi/GBM
Authors: Xue-Zhao Chang, Hou-Jun Lü, Xing Yang, Jia-Ming Chen, En-Wei Liang
Figure caption: Similar to Figure 3, but for GRB 161218B.
Paper: Six Maxims of Statistical Acumen for Astronomical Data Analysis
Authors: Hyungsuk Tak, Yang Chen, Vinay L. Kashyap, Kaisey S. Mandel, Xiao-Li Meng, Aneta Siemiginowska, David A. van Dyk
Figure caption: The posterior distributions obtained under two different models for the time delay given the same data of two gravitationally lensed light curves of Q0957+561 (L. J. Hainline et al. 2012). The model producing the posterior distribution in the top panel does not account for the effect of microlensing, producing multiple modes (H. Tak et al. 2018b), even though the tiny mode near 400 days is of scientific interest. The modes near 1100 days do not appear in the bottom panel where the model additionally accounts for the microlensing effect via polynomial regression (H. Tak et al. 2017b). It turns out that the modes near 1100 days in the top panel are spurious caused by model misspecification.
Paper: Six Maxims of Statistical Acumen for Astronomical Data Analysis
Authors: Hyungsuk Tak, Yang Chen, Vinay L. Kashyap, Kaisey S. Mandel, Xiao-Li Meng, Aneta Siemiginowska, David A. van Dyk
Figure caption: A history of Hubble constant estimates made between 1920 and 2008. The dashed horizontal line indicates the H 0 estimate at 50 km s−1 Mpc−1, and the dotted horizontal line represents the estimate at 100 km s−1 Mpc−1. The half lengths of vertical bars around dots represent 1σ uncertainty. This figure is generated using the estimates of H 0 compiled by John P. Huchra from the literature as part of the NASA/Hubble Space Telescope Key Project on the Extragalactic Distance Scale (https://lweb.cfa.harvard.edu/~dfabricant/huchra/hubble.plot.dat).
Paper: The Statistical Analysis of GeV Spectral Breaks in Bright Gamma-Ray Flat-spectrum Radio Quasars
Authors: K. R. Zhu, J. M. Chen, L. Zhang
Figure caption: Energy distribution of simulated BLR emission-line photons. The black solid line represents the Gaussian fit to the energy distribution, and the black dashed lines indicate the 2σ confidence interval. The colored regions highlight the five strongest emission lines and recombination continua identified in J. Poutanen & B. Stern (2010), specifically: R1, high-ionization He II complex; R2, hydrogen Ly lines and recombination continuum; R3, low-ionization He I 20 eV complex; R4, low-ionization O VII and C V; R5, high-ionization O VIII and C VI.
Paper: The Statistical Analysis of GeV Spectral Breaks in Bright Gamma-Ray Flat-spectrum Radio Quasars
Authors: K. R. Zhu, J. M. Chen, L. Zhang
Figure caption: The relationship between the emission region radius R, the energy density of the external photon field ﹩{{u}}_{\mathrm{ext}}^{{\prime} }﹩, the electron spectrum break energy γ  b , and the GeV spectral break energy ﹩{{E}}_{b}^{{\prime} }﹩ is shown in (a) the whole external photon field, (b) the BLR-dominated region, and (c) the DT-dominated region. The gray dashed line represents the GeV break energy detected in context.
Paper: The Statistical Analysis of GeV Spectral Breaks in Bright Gamma-Ray Flat-spectrum Radio Quasars
Authors: K. R. Zhu, J. M. Chen, L. Zhang
Figure caption: The BPL fitting results for the four brightest FSRQs in the 0.1–10 GeV range (red line) and the 0.1–50 GeV range (blue line) are depicted for (a) 3C 454.3, (b) CTA 102, (c) 3C 279, and (d) PKS 1421–41. The inset provides a detailed view near the spectral break energy.
Paper: The Statistical Analysis of GeV Spectral Breaks in Bright Gamma-Ray Flat-spectrum Radio Quasars
Authors: K. R. Zhu, J. M. Chen, L. Zhang
Figure caption: Parameter distribution of the LP-preferred FSRQs (red points) and BPL-preferred FSRQs (green points). The text in the figure indicates the KS test statistics (D) and p-values (p) in the parameter space.
Paper: The Statistical Analysis of GeV Spectral Breaks in Bright Gamma-Ray Flat-spectrum Radio Quasars
Authors: K. R. Zhu, J. M. Chen, L. Zhang
Figure caption: The relationship between the observed spectral index change Δγ and the disk luminosity ﹩{({{L}}_{46})}^{1/2}﹩. The blue line is the fitted line of Equation (10). The Spearman correlation coefficient between the two parameters is 0.171.
Paper: The Statistical Analysis of GeV Spectral Breaks in Bright Gamma-Ray Flat-spectrum Radio Quasars
Authors: K. R. Zhu, J. M. Chen, L. Zhang
Figure caption: BIC after GMM clustering with different number of clusters (n_clusters).
Paper: The Value-added Catalog of OB Stars in LAMOST DR7
Authors: Zhicun Liu, Wenyuan Cui, Jiajia Gu, Jianrong Shi, Guozhen Hu, Xiao-Long Wang, Zhenyan Huo
Figure caption: Distribution of 16,110 OB stars in the spectral subtype vs. luminosity class plane. The spectral types of these stars are obtained using MKCLASS with quality evaluation better than “good” and including “good.” The numbers in the different color boxes represent the number of stars in this spectral-type range.
Paper: The Value-added Catalog of OB Stars in LAMOST DR7
Authors: Zhicun Liu, Wenyuan Cui, Jiajia Gu, Jianrong Shi, Guozhen Hu, Xiao-Long Wang, Zhenyan Huo
Figure caption: The distribution of 5,731,921 LAMOST DR7 spectra with S/N g  ≥ 15 (gray contours), 21,529 OB spectra from Paper I (blue dots), and 9924 OB spectra selected from Xiang2022 (green dots) in the EWHγ  vs. EWCa IIK plane. The red solid lines indicate the cuts used to remove contaminants.
Paper: The Value-added Catalog of OB Stars in LAMOST DR7
Authors: Zhicun Liu, Wenyuan Cui, Jiajia Gu, Jianrong Shi, Guozhen Hu, Xiao-Long Wang, Zhenyan Huo
Figure caption: Sample of B-type spectra that MKCLASS does not give a determined spectral type, such as a Be-type star (first spectrum), a He-strong B-type star (second spectrum), a B-type star with low S/N (third spectrum), and peculiar stars (fourth spectrum). Some important line features of B-type star spectra are also marked in the picture.
Paper: The Value-added Catalog of OB Stars in LAMOST DR7
Authors: Zhicun Liu, Wenyuan Cui, Jiajia Gu, Jianrong Shi, Guozhen Hu, Xiao-Long Wang, Zhenyan Huo
Figure caption: Histogram of the MKCLASS classification results of 27,643 OB stars. The performance of classification (“excellent,” “verygood,” “good,” “fair,” and “poor”) given by MKCLASS are marked using different color bars for these stars with spectral types or luminosity types.
Paper: The Value-added Catalog of OB Stars in LAMOST DR7
Authors: Zhicun Liu, Wenyuan Cui, Jiajia Gu, Jianrong Shi, Guozhen Hu, Xiao-Long Wang, Zhenyan Huo
Figure caption: The distribution of the 200,722 LAMOST DR7 OB star candidates (gray contours), 21,529 OB spectra from Paper I (blue dots), and 9924 OB spectra selected from Xiang2022 (green dots) in the EWHγ  vs. EWFe plane. The red solid lines indicate the cuts used to remove contaminants.
Paper: The Value-added Catalog of OB Stars in LAMOST DR7
Authors: Zhicun Liu, Wenyuan Cui, Jiajia Gu, Jianrong Shi, Guozhen Hu, Xiao-Long Wang, Zhenyan Huo
Figure caption: The example spectra of O-type (top) and B-type stars (bottom) in our LAMOST catalog. Some important line features, such as He I 4009, 4026, 4387, 4471, He II 4200, 4541, 4686 Å, and C II 4267 Å, are also marked in the plots. Their spectral types obtained by manual classification are also shown in the figure.