adsabs/AstroCLIMB huggingface dataset:
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 steps for the identification of OB stars.
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: Panels from top to bottom show the uncertainty distribution of the multiply observed OB stars with different spectral (S/N) g  ranges in the spectral class (left panels) and luminosity class (right panels). The mean value, 15th percentile, and 85th percentile of spectral-type uncertainties are marked using, respectively, black-, red-, and yellow-dashed lines in the left panels. The values of the 15th and 85th percentiles of the spectral-type uncertainties are also marked in parentheses (0.2 corresponding to two spectral subtypes). Similar to the left panels, the right panels show the distribution of luminosity-type uncertainties (2.0 corresponding to two luminosity subtypes).
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 20,581 OB stars in the Galactic X–Y plane (left panel) and R–Z plane (right panel). The red cross represents the position of the Sun (X = 8.33 kpc, Y = 0 kpc, Z = 0 kpc). The yellow-dashed rings delineate constant distances from the Sun in steps of 2 kpc. The color bar corresponds to the number of stars.
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: Left pie chart: the proportions are shown for 36,782 non-OB stars (74.89%) and 12,332 OB stars (25.11%) among 49,114 common stars. Right histogram: the ratio of 137 OB stars (1.2%) not in our samples and 12,195 OB stars (98.8%) in our samples.
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 spectral types of 10,252 and 16,610 OB stars in LAMOST DR5 and DR7, respectively.
Paper: Ultra-long-period Cepheids as Standard Candles from Gaia to Rubin-LSST
Authors: Ilaria Musella, S. Leccia, R. Molinaro, M. Marconi, F. Cusano, M. Di Criscienzo, G. Fiorentino, V. Braga, V. Ripepi, G. De Somma, M. Gatto, E. Luongo, T. Sicignano
Figure caption: Gaia light curves G (green open circles), G BP (blue open circles), and G RP (red open circles) for ULPs by C.-C. Ngeow et al. (2015; top panel), N. Taneva et al. (2020; middle panel), and I. Soszyński et al. (2024; bottom panel), respectively. The G BP light curve is shifted by –2 mag. See text for details.
Paper: Ultra-long-period Cepheids as Standard Candles from Gaia to Rubin-LSST
Authors: Ilaria Musella, S. Leccia, R. Molinaro, M. Marconi, F. Cusano, M. Di Criscienzo, G. Fiorentino, V. Braga, V. Ripepi, G. De Somma, M. Gatto, E. Luongo, T. Sicignano
Figure caption: Comparison between the ULP PW relations obtained by M22 (top panel) and those derived in this paper (bottom panel) for the different selections adopted by M22 (red solid line for all ULPs, red dashed line including only ULPs with ﹩\mathrm{log}P\lt 2.15﹩, and blue solid line for the homogeneous ULP sample by A. G. Riess et al. 2016) and compared to the OGLE LMC CC one (black solid line) and the J. C. Bird et al. (2009) ULP relation (black dashed line). Note that the position of the new MW variable (cyan filled dot in the bottom panel) is in perfect agreement with the CC and ULP relations. See text for details.
Paper: Ultra-long-period Cepheids as Standard Candles from Gaia to Rubin-LSST
Authors: Ilaria Musella, S. Leccia, R. Molinaro, M. Marconi, F. Cusano, M. Di Criscienzo, G. Fiorentino, V. Braga, V. Ripepi, G. De Somma, M. Gatto, E. Luongo, T. Sicignano
Figure caption: Recovered period and light curve as a function of the years for model C in NGC 300. The top panels show the periodograms obtained after 2 (on the left) and 10 (on the right) years of the survey, using the single bands separately (colored solid lines) or adopting a multifilter approach (black solid line). The bottom panels display the simulated phase points and the input models (orange lines) in all the bands after 2 (on the left) and 10 (on the right) years, obtained by adopting the corresponding periods. See text for details.
Paper: Ultra-long-period Cepheids as Standard Candles from Gaia to Rubin-LSST
Authors: Ilaria Musella, S. Leccia, R. Molinaro, M. Marconi, F. Cusano, M. Di Criscienzo, G. Fiorentino, V. Braga, V. Ripepi, G. De Somma, M. Gatto, E. Luongo, T. Sicignano
Figure caption: Amplitude recovery for ugrizy bands. See text for details.
Paper: Ultra-long-period Cepheids as Standard Candles from Gaia to Rubin-LSST
Authors: Ilaria Musella, S. Leccia, R. Molinaro, M. Marconi, F. Cusano, M. Di Criscienzo, G. Fiorentino, V. Braga, V. Ripepi, G. De Somma, M. Gatto, E. Luongo, T. Sicignano
Figure caption: Differences between V Gaia and I Gaia mean magnitudes obtained in this paper and the ones adopted in M22 (labeled as old), for the ULPs included in Table 2. See text for details.
Paper: Ultra-long-period Cepheids as Standard Candles from Gaia to Rubin-LSST
Authors: Ilaria Musella, S. Leccia, R. Molinaro, M. Marconi, F. Cusano, M. Di Criscienzo, G. Fiorentino, V. Braga, V. Ripepi, G. De Somma, M. Gatto, E. Luongo, T. Sicignano
Figure caption: Period recovery. See text for details.
Paper: Ultra-long-period Cepheids as Standard Candles from Gaia to Rubin-LSST
Authors: Ilaria Musella, S. Leccia, R. Molinaro, M. Marconi, F. Cusano, M. Di Criscienzo, G. Fiorentino, V. Braga, V. Ripepi, G. De Somma, M. Gatto, E. Luongo, T. Sicignano
Figure caption: Light curves in the Rubin filters of the theoretical pulsating-star models in Table 3.
Paper: Ultra-long-period Cepheids as Standard Candles from Gaia to Rubin-LSST
Authors: Ilaria Musella, S. Leccia, R. Molinaro, M. Marconi, F. Cusano, M. Di Criscienzo, G. Fiorentino, V. Braga, V. Ripepi, G. De Somma, M. Gatto, E. Luongo, T. Sicignano
Figure caption: Mean magnitude recovery for ugrizy bands. See text for details.
Paper: Ultra-long-period Cepheids as Standard Candles from Gaia to Rubin-LSST
Authors: Ilaria Musella, S. Leccia, R. Molinaro, M. Marconi, F. Cusano, M. Di Criscienzo, G. Fiorentino, V. Braga, V. Ripepi, G. De Somma, M. Gatto, E. Luongo, T. Sicignano
Figure caption: Color recovery for r − i, r − z, r − y, i − z, and i − y. See text for details.
Paper: Ultra-long-period Cepheids as Standard Candles from Gaia to Rubin-LSST
Authors: Ilaria Musella, S. Leccia, R. Molinaro, M. Marconi, F. Cusano, M. Di Criscienzo, G. Fiorentino, V. Braga, V. Ripepi, G. De Somma, M. Gatto, E. Luongo, T. Sicignano
Figure caption: Color recovery for g − r, g − i, g − z, g − y, and z − y. See text for details.
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: A stacked bar chart representing the population distribution of various flare classes in each partition of the SWAN-SF benchmark data set over time. Flare classes X, M, C, and B are derived and confirmed using the GOES classification, whereas FQ represents instances of FQ and those classified as A class by GOES. This visualization is based on the current methodology of time series slicing used in SWAN-SF, which involves steps of 1 hr, an observation period of 12 hr, and a prediction span of 24 hr. Each slice of the MVTS is categorized according to the most intense flare reported within its prediction window.
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 demonstrates the application of both global and local normalization techniques on an MVTS data set. Global normalization is employed to preserve the relative magnitudes of time stamps across different time series, ensuring that the values are comparable between samples. This is particularly crucial in the SWAN-SF data set, where the magnitudes of values within each time series hold significant importance. Conversely, local normalization is used when the temporal dynamics or the structural patterns of a time series are more critical than the actual magnitudes of the values. This method emphasizes the importance of the time series’ shape and trends over their absolute 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: These scatter plots illustrate the distribution of two classes and their overlap. The dashed line highlights a decision boundary built by a classifier. It demonstrates the impact of class overlap on the complexity and effectiveness of the decision boundary and therefore the potential for higher classification performance on test data.
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 plot demonstrates the significant variation in minimum and maximum values across different attributes of the SWAN-SF data set. The diverse scales underscore the need for an advanced normalization technique to effectively analyze and process the data.
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: Illustration of two sampling approaches for SWAN-SF data set: the top panel (A) shows the original data set with a majority class having 60,000 samples and a minority class with 1000 samples. It demonstrates oversampling techniques (SMOTE, ADASYN, GNI, TimeGAN) applied to the minority class to match the majority class’s 60,000 samples. The bottom panel (B) depicts a combination of oversampling and undersampling, where the majority class is reduced using RUS, and TL to 9800 samples, and the minority class is augmented to 9800 samples using the same oversampling techniques.
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 timeline indicating the occurrence of ARs within each partition of SWAN-SF. The first number represents the year, while the second number denotes the month.
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 diagram illustrates the proper placement of NDBSR within a data preprocessing pipeline.
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 illustrates the sequence of imputation and normalization tasks in a data preprocessing pipeline.
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 showcases four distinct train–test sets employed in each classification experiment. This approach ensures a more comprehensive and accurate assessment of algorithms across all data set partitions.
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: Our four essential data preprocessing and sampling techniques to enhance classification performance on the SWAN-SF data set.
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 the outcomes of employing no preprocessing on the data set vs. the application of our imputation (I), our normalization (N), NDBSR, and various sampling techniques in combination. The efficacy of these techniques is evaluated using four time-series-based classifiers: LSTM, GRU, RNN, and 1D-CNN. 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: This plot presents a comparative analysis of the performance across various training and testing combinations applied to the SWAN-SF data set. “P” signifies partition. Each box represents the results of our fully preprocessed version of the SWAN-SF data set, which includes FPCKNN imputation, LSBZM normalization, and the removal of classes B and C. The analysis also incorporates four combinations of oversampling and undersampling techniques applied to eight classifiers on a specific train–test combination, resulting in each box showing the outcome of 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: This plot depicts a comparative analysis of TSS scores obtained by different classifiers on the SWAN-SF data set. Each box represents the results of a specific classifier on a fully preprocessed version of the SWAN-SF data set, which includes FPCKNN imputation, LSBZM normalization, and the removal of classes B and C. The analysis also incorporates four combinations of oversampling and undersampling techniques applied to four train–test combinations, resulting in each box representing the outcome of 16 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: This figure displays a comparison of our best results, depicted in shades of green, against the results from A. Ahmadzadeh et al. (2021), shown in shades of red. “I” stands for imputation, “N” for normalization, and “P” for partition of the SWAN-SF data set. Each plot shows the best TSS achieved for both A. Ahmadzadeh et al. (2021) and our technique.
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: PCA 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: 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:  F tot–Γ correlations of both ME and EE phases for four GRBs (081215, 100829A, 161218B, and 170626A) of our sample. The solid red lines are the best fit with the exponential function.
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:  F tot–kT correlations of both ME and EE phases for four GRBs (081215, 100829A, 161218B, and 170626A) of our sample. The solid red lines are the best fit with the power-law function.
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 of GRB 150510A and GRB 170115B. The dashed line is the limiting value of α = −2/3.
Paper: An Efficient Method for Batch Derivation of Detached Eclipsing Binary Parameters: Analysis of 34,907 OGLE Systems
Authors: JinLiang Wang, Xu Ding, Wei Liu, LiHuan Yu, Chong Xu, KaiFan Ji
Figure caption: The horizontal axis A 2 and the vertical axis A 4 represent the coefficients of the second and fourth terms in the Fourier series expansion, respectively. Classification was performed using the method provided by X. Chen et al. (2018, 2020). The colored density regions represent the classified detached binary samples, while the gray regions represent the classified semidetached binary samples.
Paper: An Efficient Method for Batch Derivation of Detached Eclipsing Binary Parameters: Analysis of 34,907 OGLE Systems
Authors: JinLiang Wang, Xu Ding, Wei Liu, LiHuan Yu, Chong Xu, KaiFan Ji
Figure caption: The posterior distribution of the six parameters (T 2/T 1, i, q, r 1, r 2, and e) for OGLE-BLG-ECL-087868 derived using the proposed method is shown. In each of the six diagonal panels, the solid vertical line denotes the median of the parameter distribution at the 50th percentile. The dashed vertical lines adjacent to each median indicate the lower and upper bounds of the 68% credible interval, which correspond to the innermost contours in the 15 off-diagonal panels. The statistical values associated with these contours are displayed in the first row above the six diagonal panels. The outermost dashed vertical lines in the diagonal panels mark the lower and upper bounds of the 95% credible interval, corresponding to the outer contours in the 15 off-diagonal panels. Statistical values related to these contours are presented in the second row above the six diagonal panels.
Paper: An Efficient Method for Batch Derivation of Detached Eclipsing Binary Parameters: Analysis of 34,907 OGLE Systems
Authors: JinLiang Wang, Xu Ding, Wei Liu, LiHuan Yu, Chong Xu, KaiFan Ji
Figure caption: The fitting results for OGLE-BLG-ECL-087868 and OGLE-BLG-ECL-406220 are presented. Upper panel: observed LCs (blue dots and gray lines) with photometric errors and PHOEBE-generated LCs (orange lines) using estimated parameters. Lower panel: residuals between the two LCs, with blue dots indicating differences at each point and a red dashed line representing the zero baseline.
Paper: An Efficient Method for Batch Derivation of Detached Eclipsing Binary Parameters: Analysis of 34,907 OGLE Systems
Authors: JinLiang Wang, Xu Ding, Wei Liu, LiHuan Yu, Chong Xu, KaiFan Ji
Figure caption: Two thousand synthetic LCs were randomly selected from the test set, and Gaussian noise with a standard deviation of 0.01 mag was added. The R 2 values for each LC were obtained separately using both the proposed method and the MCMC method. In the figure, the distribution of R 2 values obtained from the proposed method is represented by the blue solid bar, whereas the distribution from the MCMC method is indicated by the orange dashed bar.
Paper: An Efficient Method for Batch Derivation of Detached Eclipsing Binary Parameters: Analysis of 34,907 OGLE Systems
Authors: JinLiang Wang, Xu Ding, Wei Liu, LiHuan Yu, Chong Xu, KaiFan Ji
Figure caption: The fitting results for OGLE-BLG-ECL-067958 and OGLE-BLG-ECL-086683 are presented. Upper panel: observed LCs (blue dots and gray lines) with photometric errors and PHOEBE-generated LCs (orange lines) using estimated parameters. Lower panel: residuals between the two LCs, with blue dots indicating differences at each point and a red dashed line representing the zero baseline.
Paper: An Efficient Method for Batch Derivation of Detached Eclipsing Binary Parameters: Analysis of 34,907 OGLE Systems
Authors: JinLiang Wang, Xu Ding, Wei Liu, LiHuan Yu, Chong Xu, KaiFan Ji
Figure caption: A comparison of the posterior distributions of parameters for the observed LC (OGLE-BLG-ECL-406220), obtained separately using the proposed method and the MCMC method, is illustrated. The blue regions represent the parameter posterior distributions derived using the proposed method, while the red regions denote those obtained using the MCMC method.
Paper: An Efficient Method for Batch Derivation of Detached Eclipsing Binary Parameters: Analysis of 34,907 OGLE Systems
Authors: JinLiang Wang, Xu Ding, Wei Liu, LiHuan Yu, Chong Xu, KaiFan Ji
Figure caption: A comparison between the synthetic LCs generated by the NN model and those produced by the PHOEBE is illustrated. The upper part of the figure displays the blue LC generated by PHOEBE and the orange LC generated by the NN model. The lower part of the figure shows blue dots representing the residuals between corresponding points of the two LCs, while the red dashed line indicates the zero value baseline.
Paper: An Efficient Method for Batch Derivation of Detached Eclipsing Binary Parameters: Analysis of 34,907 OGLE Systems
Authors: JinLiang Wang, Xu Ding, Wei Liu, LiHuan Yu, Chong Xu, KaiFan Ji
Figure caption: The structure of the NN model consists of seven input parameters, four hidden layers, and an output layer comprising synthetic LCs with 150 phase points. The numbers within the boxes indicate the number of nodes in each layer.
Paper: An Efficient Method for Batch Derivation of Detached Eclipsing Binary Parameters: Analysis of 34,907 OGLE Systems
Authors: JinLiang Wang, Xu Ding, Wei Liu, LiHuan Yu, Chong Xu, KaiFan Ji
Figure caption: A density plot showing the distribution of R 2 and RMSE values for 35,962 detached EBs. Colors represent the number of targets in each bin.
Paper: An Empirical Sample of Spectra of M-type Stars with Homogeneous Atmospheric-parameter Labels
Authors: Bing Du, A-Li Luo, Song Wang, Yinbi Li, Cai-Xia Qu, Xiao Kong, Yan-xin Guo, Yi-han Song, Fang Zuo
Figure caption: Color–magnitude diagram of the primary sample of M giants. The error bars represent the average values and the 1σ uncertainties of G magnitudes with a color step of 0.5 mag, and the black dashed curve is the third-order polynomial fit to the average values. The red-dashed curves are created by shifting the black curve up and down by 1σ uncertainty on G magnitude. The objects lying within the red-dashed enclosed region are selected.
Paper: An Empirical Sample of Spectra of M-type Stars with Homogeneous Atmospheric-parameter Labels
Authors: Bing Du, A-Li Luo, Song Wang, Yinbi Li, Cai-Xia Qu, Xiao Kong, Yan-xin Guo, Yi-han Song, Fang Zuo
Figure caption: Histograms of differences between ASPCAP and StarHorse (top row), Ding22 and StarHorse (middle row), and LASPM and StarHorse (bottom row). The red-dashed curves are Gaussian fits to the distributions, and the mean and dispersion of the Gaussian fits to the mean and standard deviation values of the differences.