adsabs/AstroCLIMB huggingface dataset:
Paper: Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data
Authors: Zhijian Luo, Jianzhen Chen, Zhu Chen, Shaohua Zhang, Liping Fu, Hubing Xiao, Chenggang Shu
Figure caption: Variation of ECE and Brier score with the proportion of labeled data used during training. The solid lines (red for ECE, cyan for Brier score) correspond to results obtained on the holdout test set, while the dashed lines (blue for ECE, green for Brier score) show variations in these metrics across the test sets of different data partitioning schemes.
Paper: Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data
Authors: Zhijian Luo, Jianzhen Chen, Zhu Chen, Shaohua Zhang, Liping Fu, Hubing Xiao, Chenggang Shu
Figure caption: Variation in classification accuracy of the GC-SWGAN model with the proportion of labeled data used during training. The red solid line denotes the accuracy on the holdout test set, while the blue dashed line represents the accuracy across the test sets of different data partitioning schemes.
Paper: Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data
Authors: Zhijian Luo, Jianzhen Chen, Zhu Chen, Shaohua Zhang, Liping Fu, Hubing Xiao, Chenggang Shu
Figure caption: Confusion matrix for the target test set under the Train_test_91 scheme, illustrating confusion among morphologically similar classes.
Paper: Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data
Authors: Zhijian Luo, Jianzhen Chen, Zhu Chen, Shaohua Zhang, Liping Fu, Hubing Xiao, Chenggang Shu
Figure caption: The detailed structures of the generator G, discriminator D, and classifier C in the GC-SWGAN model.
Paper: Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data
Authors: Zhijian Luo, Jianzhen Chen, Zhu Chen, Shaohua Zhang, Liping Fu, Hubing Xiao, Chenggang Shu
Figure caption: Random example images of each class in Galaxy10 DECaLS.
Paper: Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data
Authors: Zhijian Luo, Jianzhen Chen, Zhu Chen, Shaohua Zhang, Liping Fu, Hubing Xiao, Chenggang Shu
Figure caption: Some example images of Merging Galaxies in the unlabeled DECaLS data set.
Paper: Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data
Authors: Zhijian Luo, Jianzhen Chen, Zhu Chen, Shaohua Zhang, Liping Fu, Hubing Xiao, Chenggang Shu
Figure caption: Representative images of predicted categories in the unlabeled DECaLS data set.
Paper: Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data
Authors: Zhijian Luo, Jianzhen Chen, Zhu Chen, Shaohua Zhang, Liping Fu, Hubing Xiao, Chenggang Shu
Figure caption: Randomly selected samples from each category in the generated fake galaxies produced by the generator.
Paper: A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection
Authors: Junpei Li, Hong Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo, Hao Deng
Figure caption: Architecture of the proposed CNN–LSTM model. (1) Yellow arrows: 1D convolution with ReLU activation; (2) LSTM layer; (3) fully connected layer with point-wise probability output; (4) gray arrows: dynamic loss function.
Paper: A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection
Authors: Junpei Li, Hong Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo, Hao Deng
Figure caption: Observations of solar wind dynamics during an ICME by the Wind spacecraft at the L1 Lagrange point in 1997. The vertical dotted lines mark onset and termination times of an ICME event. Panels from top (1) to bottom (5) show: (1) magnetic-field-related features B, Bx, By, and Bz; (2) solar wind velocity V; (3) thermal velocity Vth; (4) proton and alpha particle densities obtained from the analysis of moments and nonlinear analyses Np, Np,nl, and Nα,nl, respectively; (5) plasma parameter β, (6) event labeling.
Paper: A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection
Authors: Junpei Li, Hong Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo, Hao Deng
Figure caption: Cumulative probabilities of correctly predicted data points within each ICME event vs. temporal shift to the closest predicted ICME event. Blue dots and red dots stand for TPs and FNs, respectively, with dot size corresponding to the duration of the ICME event; the black line indicates the minimum temporal shift of the TPs.
Paper: A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection
Authors: Junpei Li, Hong Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo, Hao Deng
Figure caption: Precision–recall comparison of different models. Green = high-precision regime (P > 0.80), purple = high-recall regime (R > 0.80); colored dots denote candidate models.
Paper: A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection
Authors: Junpei Li, Hong Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo, Hao Deng
Figure caption: Workflow of the proposed CNN–LSTM model for ICME detection. Input a temporal segment x ∈ RT × 33; raw/corrected outputs y*, y ∈ {0, 1} with 1 indicating ICME events.
Paper: A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection
Authors: Junpei Li, Hong Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo, Hao Deng
Figure caption: Histogram of time intervals between adjacent ICME events with 2 hr resolution.
Paper: A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection
Authors: Junpei Li, Hong Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo, Hao Deng
Figure caption: Comparison of F1 score before and after the application of the correction strategy.
Paper: A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection
Authors: Junpei Li, Hong Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo, Hao Deng
Figure caption: Magnetic field profiles and detection results of three ICME events: (a), (b) successful long-duration case; (c), (d) successful short-duration case; (e), (f) failed short-duration case, with vertical dotted lines marking the boundaries of ICME events.
Paper: A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection
Authors: Junpei Li, Hong Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo, Hao Deng
Figure caption: Histograms of the duration, magnetic field, and thermal velocity distributions for both test and predicted ICMEs.
Paper: A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection
Authors: Junpei Li, Hong Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo, Hao Deng
Figure caption: Duration and probability sum histograms for both TP and FP.
Paper: Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition
Authors: Zeinab Hassani, Davud Mohammadpur, Hossein Safari
Figure caption: PDF for the minimum (left panel) and maximum (right panel) for windows of peak fluxes. The red dashed line represents the power-law function, where for the minimum flux of the windows, α = 3.27 and fth = 7.76 × 10−7 W m−2; for the maximum flux of the windows, α = 1.89 and fth = 5.63 × 10−6 W m−2.
Paper: Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition
Authors: Zeinab Hassani, Davud Mohammadpur, Hossein Safari
Figure caption: (Top panel) The DLSTM extracts trends (red dashed line) and seasonal components (blue line) from the flare time series (black line) over 200 time steps. (Bottom panel) The random noise is derived by subtracting the sum of the trends and seasonal components from the flare time series.
Paper: Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition
Authors: Zeinab Hassani, Davud Mohammadpur, Hossein Safari
Figure caption: Example of sliding window w1 (green rectangle) for 24 sequence flares, including the peak flux subwindow (red rectangle) and waiting time subwindow (blue rectangle). The label 1 = 3 for w1 indicates the 25th in the flare time series, which is C-class flares.
Paper: Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition
Authors: Zeinab Hassani, Davud Mohammadpur, Hossein Safari
Figure caption: The solar flare splitting scheme for regularized time series cross-validation of Folds 1–4. For each fold, the green box represents the time span used for the training data set, while the gray box corresponds to the testing data set duration.
Paper: Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition
Authors: Zeinab Hassani, Davud Mohammadpur, Hossein Safari
Figure caption: The frequency size distribution (histogram) of daily flare number for 2003 January 11 to 2023 April 30.
Paper: Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition
Authors: Zeinab Hassani, Davud Mohammadpur, Hossein Safari
Figure caption: The flowchart for predicting small and large solar flares consists of three key steps: preprocessing (smoothing solar peak fluxes), applying a sliding window with resampling on both irregular and regular flare time series, and utilizing a prediction approach. During preprocessing, solar peak fluxes are smoothed, and peak fluxes (P) and waiting times (Wt) are normalized. Each sliding window comprises a peak flux subwindow and a waiting time subwindow, each containing a sequence of 24 flares. An oversampling algorithm is applied to address the imbalance in the minority class, corresponding to large (M and X) flares. For the prediction step, we employ six models: (1) LSTM and (2) DLSTM applied to irregular time series, (3) LSTM and (4) DLSTM applied to regular time series, and (5) LSTM and (6) DLSTM integrated with ensemble learning for regular time series.
Paper: Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition
Authors: Zeinab Hassani, Davud Mohammadpur, Hossein Safari
Figure caption: A sample of the ROC curves for six models: LSTM on irregular time series (Model 1), DLSTM on irregular time series (Model 2), LSTM on regular time series (Model 3), DLSTM on regular time series (Model 4), LSTM on regular time series with ensemble learning (Model 5), and DLSTM on regular time series with ensemble learning (Model 6). The left panel shows results without resampling (R = 0), while the right panel includes resampling (R = 12). The green dashed line represents a random classifier with no predictive skill. The AUC is calculated for each model, where an AUC of 0.5 indicates performance of random classification.
Paper: Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition
Authors: Zeinab Hassani, Davud Mohammadpur, Hossein Safari
Figure caption: The average to gather with error bar (standard deviation) TSS for LSTM (blue circle) and DLSTM (red triangle) for different K-folds of the binary forecasting model for small and large flares with resampling R = 12 of the minority class.
Paper: Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning
Authors: Viacheslav M. Sadykov, Leon Ofman, Scott A. Boardsen, Yogesh, Parisa Mostafavi, Lan K. Jian, Kristopher Klein, Mihailo Martinović
Figure caption: Examples of proton and α VDFs integrated over vz for simulation run #4 in Table 1, at t = 0﹩{{\rm{\Omega }}}_{p}^{-1}﹩ (panels (a) and (d)), t = 150﹩{{\rm{\Omega }}}_{p}^{-1}﹩ (panels (b) and (e)), and t = 300﹩{{\rm{\Omega }}}_{p}^{-1}﹩ (panels (c) and (f)). The external magnetic field is directed along the x-axis (it has a nonzero x-component). The velocities of particles are normalized to VA. Panel (g) displays the power spectrum of E⊥ normalized for every time moment separately along the duration of the run. Panel (h) displays the change rates of the anisotropies, Ap and AHe, and the perpendicular magnetic energy, WM,⊥. Red dotted lines correspond to the empirical instability threshold ±0.001Ωp used in this study. The cases for all three time moments are therefore unstable.
Paper: Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning
Authors: Viacheslav M. Sadykov, Leon Ofman, Scott A. Boardsen, Yogesh, Parisa Mostafavi, Lan K. Jian, Kristopher Klein, Mihailo Martinović
Figure caption: Box-and-whiskers plots summarizing the accuracy scores (left) and the TSS scores (right) of binary classification models considered in this work, over 10 randomized train-test data splits. Each colored rectangle spans through the second and third quartiles of the scores, the horizontal bar marks the median. The whiskers indicate the locations of the last individual results within the interquartile range from the boxes. The rhombus points mark outliers outside the interquartile range from the boxes.
Paper: Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning
Authors: Viacheslav M. Sadykov, Leon Ofman, Scott A. Boardsen, Yogesh, Parisa Mostafavi, Lan K. Jian, Kristopher Klein, Mihailo Martinović
Figure caption: Same as Figure 3 but with the inclusion of the dynamic power spectrum information.
Paper: Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning
Authors: Viacheslav M. Sadykov, Leon Ofman, Scott A. Boardsen, Yogesh, Parisa Mostafavi, Lan K. Jian, Kristopher Klein, Mihailo Martinović
Figure caption: Architecture of the best-performing CNN working with the VDFs of 0.10VA resolution with power spectrum information included.
Paper: Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning
Authors: Viacheslav M. Sadykov, Leon Ofman, Scott A. Boardsen, Yogesh, Parisa Mostafavi, Lan K. Jian, Kristopher Klein, Mihailo Martinović
Figure caption: Visualization of prediction outcomes across 10 validation data sets for the RF classifier with the utilization of E⊥ dynamic power spectrum. Panel (a) presents the rates of the magnetic energy change for the correct predictions and incorrect predictions. The black dashed line represents the empirical instability threshold used in this work. Panel (b) visualizes the results in the T⊥/T∥, p, and β∥, p space. TP predictions are shown in green, TNs in black, FPs in red, and FNs in orange. Panel (c) demonstrated the accuracy of the feature-based models depending on the closeness of the change rate of the anisotropies or the perpendicular magnetic energy (whichever is greater) to the empirical instability threshold of 10−3Ωp. The dashed and dotted black and red lines in panel (b) represent the theoretical thresholds for the anisotropy-driven ion-kinetic instabilities following D. Verscharen et al. (2016) for the growth rate of γm = 10−3Ωp: ion cyclotron (black dotted), mirror (black dashed), fast magnetosonic/whistler (red dotted), and oblique firehose (red dashed).
Paper: Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning
Authors: Viacheslav M. Sadykov, Leon Ofman, Scott A. Boardsen, Yogesh, Parisa Mostafavi, Lan K. Jian, Kristopher Klein, Mihailo Martinović
Figure caption: Accuracy of the considered classifiers on the “unseen” hybrid-PIC runs (visualized for each hybrid-PIC run on which the classifiers validate and do not train) for the instability threshold of 0.001Ωp. The absence of the bar for a certain ML model and Hybrid-PIC run indicates that its accuracy was zero (i.e., all predictions were incorrect).
Paper: Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning
Authors: Viacheslav M. Sadykov, Leon Ofman, Scott A. Boardsen, Yogesh, Parisa Mostafavi, Lan K. Jian, Kristopher Klein, Mihailo Martinović
Figure caption: Visualization of the evolution of the hybrid-PIC runs in the parameter spaces of (a) proton temperature anisotropy and β∣∣, (b) α temperature anisotropy and β∣∣, and (c) proton and α average velocities along the direction of the external magnetic field. Colored points represent the evolutions of runs for which the accuracy was lower than 50% for at least three ML models. The black semitransparent points represent the rest of the runs. The dashed and dotted black and red lines in panel (a) represent the theoretical thresholds for the anisotropy-driven ion-kinetic instabilities following D. Verscharen et al. (2016) for the growth rate of γm = 10−3Ωp: ion cyclotron (black dotted), mirror (black dashed), fast magnetosonic/whistler (red dotted), and oblique firehose (red dashed).
Paper: Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning
Authors: Viacheslav M. Sadykov, Leon Ofman, Scott A. Boardsen, Yogesh, Parisa Mostafavi, Lan K. Jian, Kristopher Klein, Mihailo Martinović
Figure caption: Same as in Figure 5 but with the inclusion of the power spectrum.
Paper: Measurements of Velocity-dependent Single- and Double-electron-capture Cross Sections for O7+ Ions Impacting He, H2, N2, O2, and CH4
Authors: T. Meng, Y. Wu, H. Yin, X. Tan, B. Ren, P. Ma, B. Tu, K. Yao, J. Xiao, Y. Zou, B. Wei
Figure caption: The total cross sections of SEC (solid symbols) and DEC (hollow symbols) for O7+ colliding with N2, O2, and CH4 as a function of impact energy.
Paper: Measurements of Velocity-dependent Single- and Double-electron-capture Cross Sections for O7+ Ions Impacting He, H2, N2, O2, and CH4
Authors: T. Meng, Y. Wu, H. Yin, X. Tan, B. Ren, P. Ma, B. Tu, K. Yao, J. Xiao, Y. Zou, B. Wei
Figure caption: The total cross sections of SEC (solid symbols) and DEC (hollow symbols) for O7+ colliding with He and H2 as a function of impact energy.
Paper: Measurements of Velocity-dependent Single- and Double-electron-capture Cross Sections for O7+ Ions Impacting He, H2, N2, O2, and CH4
Authors: T. Meng, Y. Wu, H. Yin, X. Tan, B. Ren, P. Ma, B. Tu, K. Yao, J. Xiao, Y. Zou, B. Wei
Figure caption: Schematic diagram of the experimental apparatus.
Paper: TESS Light Curves of Cataclysmic Variables. VI. Intermediate Polars
Authors: Albert Bruch
Figure caption: Top: low-frequency power spectra of the light curves of V455 And, dominated by the signal of a negative superhump. The frequency of an alleged positive superhump (S. Araujo-Betancor et al. 2005) is marked by vertical bars. LC#2 contains additional signals of unknown origin, absent in the other light curves of V455 And. Bottom: time-resolved power spectrum of LC#2 in the frequency range of the additional signals of unknown origin. The color bar is representative for all color coded time-resolved power spectra or waveforms (except Figure 46) of this paper. For more explanations, see Section 2.
Paper: TESS Light Curves of Cataclysmic Variables. VI. Intermediate Polars
Authors: Albert Bruch
Figure caption: Left: orbital waveform of TESS light curve LC#2 of BG CMi (top), and WD spin waveforms of both light curves (bottom). Top right: temporal evolution of the WD spin period of BG CMi taken from J. Patterson et al. (2020; black dots) together with the new period measurements based on the TESS data (in blue). Bottom right: power spectra of the two TESS light curves at high frequencies, with a 253 s signal unrelated to other periodicities in the system.
Paper: TESS Light Curves of Cataclysmic Variables. VI. Intermediate Polars
Authors: Albert Bruch
Figure caption: Average orbital waveforms of LC#1 and LC#2 of FO Aqr (top left and middle panels), the temporal evolution of the WD spin period (top right; the black dots are from J. Patterson et al. 2020, the blue ones refer to the Kepler and TESS light curves, respectively), light curve LC#1, and the time-resolved WD spin and orbital waveforms constructed from LC#1 (lower panels).
Paper: TESS Light Curves of Cataclysmic Variables. VI. Intermediate Polars
Authors: Albert Bruch
Figure caption: The upper panels show in black the power spectra of the two TESS light curves of the DW Cnc, normalized to the power of the WD spin signal. Overplotted in red are the same spectra multiplied by a factor of 5 in order to better visualize faint structures. The panels in the third row contain the waveforms of the orbit (left), spin-orbital sideband (middle, averaged over both light curves), and negative superhump variations (right, only seen in LC#2). The lower panels contain the time-resolved orbital (left) and WD spin (right) waveforms of LC#2.
Paper: TESS Light Curves of Cataclysmic Variables. VI. Intermediate Polars
Authors: Albert Bruch
Figure caption: Left: WD spin period of V647 Aur as a function of time. The black data points are from V. P. Kozhevnikov (2014); the blue ones measured in the TESS light curves. The red line is a linear least-squares fit to the data. Middle: a 10 day section of LC#2 of V457 Aur, containing strong QPOs on the timescale of several hours. Right: low-frequency part of the power spectrum of the entire LC#2 of V457 Aur with the orbital signal and strong signals at the QPO frequencies.
Paper: TESS Light Curves of Cataclysmic Variables. VI. Intermediate Polars
Authors: Albert Bruch
Figure caption: Left: orbital waveforms of TESS light curves LC#1–LC#7 (from bottom to top) of MU Cam. Top right: average WD spin waveform of MU Cam. Bottom right: WD spin period of MU Cam as a function of time together with a linear least-squares fit to the data. The black data points were taken from the literature; the blue ones refer to the TESS light curves.
Paper: TESS Light Curves of Cataclysmic Variables. VI. Intermediate Polars
Authors: Albert Bruch
Figure caption: Left: power spectrum of LC#2 of V405 Aur in the frequency range around the (truncated) first overtone of the WD spin signal. Right: average WD spin waveform of V405 Aur.
Paper: TESS Light Curves of Cataclysmic Variables. VI. Intermediate Polars
Authors: Albert Bruch
Figure caption: Top row: power spectrum of LC#1 of PQ Gem. The inset contains the frequency range around the F2 signal on an expanded scale. Second row: orbital (left) and white dwarf spin waveforms (right) observed in the three TESS light curves. Third row: power spectra of the three light curves in a small frequency range around unexplained periodicities at F1 (left) and F2 (right). For details, see text. Bottom: time-resolved power spectrum of the frequency range around the F1 signal in LC#2. The periodic disturbances are caused by gaps in the light curve.
Paper: TESS Light Curves of Cataclysmic Variables. VI. Intermediate Polars
Authors: Albert Bruch
Figure caption: Top: the temporal evolution of the WD spin period in DQ Her. The black dots are values taken from J. Patterson et al. (2020); the two blue dots at the right represent periods measured in the TESS light curves. The solid and dashed lines are quadratic least-squares fits to all data (red), the data before 2005 (green), and the data after 2005 (blue). The inset shows the evolution after 1998 on an extended vertical scale in order to highlight a change of the second derivative of the WD spin period, which may have taken place around 2005. Bottom: the residuals of a linear fit to the data in the upper panel.