adsabs/AstroCLIMB huggingface dataset:
Paper: Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches
Authors: Hamed Ghaderi, Nasibe Alipour, Hossein Safari
Figure caption: To samples of elliptical galaxies (left column) and the corresponding ZMs with ﹩{p}_{{\rm{\max }}}\,=\,45﹩ (right column) for Sérsic index n = 2 (top row) and n = 4 (bottom row). The ﹩{p}_{{\rm{\max }}}\,=\,45﹩ returns 1081 ZMs with the set of orders and repetitions labeled by (p, q).
Paper: Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches
Authors: Hamed Ghaderi, Nasibe Alipour, Hossein Safari
Figure caption: The Zernike polynomials (ZPs) for different order numbers p = 0, 1, 2, and 3 in polar coordinates.
Paper: Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches
Authors: Hamed Ghaderi, Nasibe Alipour, Hossein Safari
Figure caption: (Top panel) the simulated spiral galaxy profile with Sérsic index (n = 1), Re = 100, and Ie = 18 for inclination angle i = 0° (left) and i = 60° (right), (middle panel) the ZMs with ﹩{p}_{{\rm{\max }}}\,=\,45﹩ for two perspectives, and (bottom panel) the scatter plot of ZMs for spiral galaxy with i = 0° (face on) and i = 60°. The inset (middle panel) indicates a large view for a portion of ZMs for two perspectives of the spiral galaxy.
Paper: Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches
Authors: Hamed Ghaderi, Nasibe Alipour, Hossein Safari
Figure caption: The probability density function (PDF) of the root square ZMs of Z3 for 4077 elliptical (solid blue line), 6139 spiral (dashed orange line), and 1519 odd objects (green dots) collected by GZ2. We illustrate the discriminant boundary between the PDF of elliptical with spiral (vertical solid red line), elliptical with odd objects (vertical dashed red line), and spiral with odd objects (vertical dotted red line).
Paper: Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches
Authors: Hamed Ghaderi, Nasibe Alipour, Hossein Safari
Figure caption: The PDF of Z3 for 545 nongalaxy images (dashed orange line) and 780 galaxy images (solid blue line). The vertical solid red line indicates the discriminant boundary of Z3 for galaxy and nongalaxy samples.
Paper: Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches
Authors: Hamed Ghaderi, Nasibe Alipour, Hossein Safari
Figure caption: The receiver operating characteristic (ROC) curve for Model I (blue), Model II (orange), Model III (green), Model IV (red), and Model V (purple) of galaxy–nongalaxy classification and random (black dashed line). The area under the curve (AUC) is obtained to be 0.83 ± 0.02, 0.78 ± 0.02, 0.71 ± 0.02, 0.80 ± 0.02, and 0.82 ± 0.02 for Model I, Model II, Model III, Model IV, Model V, respectively.
Paper: Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches
Authors: Hamed Ghaderi, Nasibe Alipour, Hossein Safari
Figure caption: A sample confusion matrix for Model I representing the predicted class vs. true class. True positive (TP) shows the number of galaxies that are correctly classified in galaxy class (positive class). In contrast, false positive (FP) indicates the number of nongalaxy images that are wrongly classified in the galaxy class. True negative (TN) shows the number of nongalaxies that are correctly classified in the nongalaxy class (negative class). In contrast, false negative (FN) indicates the number of galaxy images that are wrongly classified in the nongalaxy class.
Paper: Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches
Authors: Hamed Ghaderi, Nasibe Alipour, Hossein Safari
Figure caption: (Left panel) the simulated spiral galaxies with the arm amplitude of 0.0 (a), 0.2 (b), and 0.8 (c), (right panel) the ZMs with pmax = 4 for each simulated spiral galaxy. The other parameters for three simulated spiral galaxies, including the Sérsic index n, effective intensity, effective radius, number of arms, and rotation term are 1, 18.0, 500, 2, and 0.1, respectively.
Paper: Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches
Authors: Hamed Ghaderi, Nasibe Alipour, Hossein Safari
Figure caption: The variation of ZMs ﹩{Z}_{0}^{0}﹩ (black line), ﹩{Z}_{2}^{0}﹩ (blue line), ﹩{Z}_{4}^{0}﹩ (orange line), ﹩{Z}_{6}^{0}﹩ (green line), ﹩{Z}_{8}^{0}﹩ (red line), and ﹩{Z}_{10}^{0}﹩ (purple line) vs. ellipticity parameter of the elliptical galaxies with n = 2 (left panel) and n = 4 (right panel).
Paper: Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches
Authors: Hamed Ghaderi, Nasibe Alipour, Hossein Safari
Figure caption: The original image recorded by SDSS (left panel), segmented galaxy represented within a green rectangle (middle panel), galaxy subimage (right panel).
Paper: Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches
Authors: Hamed Ghaderi, Nasibe Alipour, Hossein Safari
Figure caption: The schematic table of the confusion matrix represents the actual label vs. predicted labels for a three-class model.
Paper: Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches
Authors: Hamed Ghaderi, Nasibe Alipour, Hossein Safari
Figure caption: The ROC curve for spiral (left panel), elliptical (middle panel), and odd objects (right panel) classes of Model I (blue), Model II (orange), Model III (green), Model IV (red), and Model V (purple), and random (black dashed line). The area under the curve (AUC) is obtained for each class and every model.
Paper: Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches
Authors: Hamed Ghaderi, Nasibe Alipour, Hossein Safari
Figure caption: Variation of accuracy (orange) and TSS (blue) vs. absolute magnitudes of galaxies (AM).
Paper: GR-Athena++: General-relativistic Magnetohydrodynamics Simulations of Neutron Star Spacetimes
Authors: William Cook, Boris Daszuta, Jacob Fields, Peter Hammond, Simone Albanesi, Francesco Zappa, Sebastiano Bernuzzi, David Radice
Figure caption: Waveform analysis and comparison with BAM. Top: real part and amplitude of the (ℓ, m) = (2, 2) mode of the strain. We show GR-Athena++ data with WENOZ reconstruction and N = 64 and BAM data with the finite-volume scheme and WENOZ reconstruction and with the EFL scheme at a similar resolution. The y-axis is plotted in geometric units. Middle: instantaneous frequency of the waves for the three data sets. The y-axis is plotted in geometric units. Bottom: phase differences between the GR-Athena++ data with N = 64 and GR-Athena++ data at higher resolutions of N = 96 (dashed) and N = 128 (dotted lines), and BAM data. The GR-Athena++ phase difference data at N = 128 are rescaled assuming second-order convergence, matching with the N = 96 data up to merger. The moment of merger of each data set is shown as a vertical line.
Paper: GR-Athena++: General-relativistic Magnetohydrodynamics Simulations of Neutron Star Spacetimes
Authors: William Cook, Boris Daszuta, Jacob Fields, Peter Hammond, Simone Albanesi, Francesco Zappa, Sebastiano Bernuzzi, David Radice
Figure caption: Binding energy vs. angular momentum curves E  b (j) for GR-Athena++ and BAM simulations. These quantities are computed following S. Bernuzzi et al. (2012), and T. Damour et al. (2012); they are rescaled by the mass, and shown in geometric units. The moment of merger of each data set is shown as vertical line. The inset zooms in on the energetics during the orbital phase, prior to the moment of merger. Note the initial adjustment related to the use of conformally flat initial data and “junk” radiation.
Paper: GR-Athena++: General-relativistic Magnetohydrodynamics Simulations of Neutron Star Spacetimes
Authors: William Cook, Boris Daszuta, Jacob Fields, Peter Hammond, Simone Albanesi, Francesco Zappa, Sebastiano Bernuzzi, David Radice
Figure caption: Snapshots of rest-mass density (upper half) and magnetic field strength (lower half) in the orbital plane. The snapshots correspond to the final orbit (t = 5.96 ms), the moment of merger (t = 7.83 ms), conventionally defined as the moment at which the (ℓ, m) = (2, 2) mode of the GW has the amplitude peak, and a late stage shortly before gravitational collapse t = 16 ms. White contours are lines of constant density at values ρ = (6.18 × 1014, 3.09 × 1014, 6.18 × 1013, 6.18 × 1012, 6.18 × 1010, 6.18 × 108, 6.18 × 106, 6.18 × 104) g cm−3 .
Paper: GR-Athena++: General-relativistic Magnetohydrodynamics Simulations of Neutron Star Spacetimes
Authors: William Cook, Boris Daszuta, Jacob Fields, Peter Hammond, Simone Albanesi, Francesco Zappa, Sebastiano Bernuzzi, David Radice
Figure caption: Top: evolution of baryon mass and BH (Christodoulou) mass in the collapsing D4 model for N = 32. The dashed line shows the sum of the BH mass and the GW energy in the (2, 0) mode extracted at the coordinate sphere. Bottom: baryon mass and angular momentum conservation and the relative differences of the Christodoulou mass and the sum of the Christodoulou mass and the radiated energy in GWs with respect to the initial ADM mass.
Paper: GR-Athena++: General-relativistic Magnetohydrodynamics Simulations of Neutron Star Spacetimes
Authors: William Cook, Boris Daszuta, Jacob Fields, Peter Hammond, Simone Albanesi, Francesco Zappa, Sebastiano Bernuzzi, David Radice
Figure caption: Evolution of central rest-mass density and lapse of collapsing D4 model. Data from PPM simulations are shown at different resolutions. Vertical black lines denote the collapse time for each simulation. We note that the oscillatory behavior occurs within the horizon, after collapse.
Paper: GR-Athena++: General-relativistic Magnetohydrodynamics Simulations of Neutron Star Spacetimes
Authors: William Cook, Boris Daszuta, Jacob Fields, Peter Hammond, Simone Albanesi, Francesco Zappa, Sebastiano Bernuzzi, David Radice
Figure caption: Baryon mass conservation along the GRHD and GRMHD evolutions of the BNS model. To the moment of merger, t ≃ 7.5 ms, the maximum relative violation is of the order 10−7. After merger, the violation increases to the 10−6 level. Note the mass typically increases rather than decreasing due to the accretion of the atmosphere. The sudden drop of the mass at late times is due to BH formation.
Paper: GR-Athena++: General-relativistic Magnetohydrodynamics Simulations of Neutron Star Spacetimes
Authors: William Cook, Boris Daszuta, Jacob Fields, Peter Hammond, Simone Albanesi, Francesco Zappa, Sebastiano Bernuzzi, David Radice
Figure caption: Top: growth of the toroidal component of the magnetic field energy as a fraction of the total magnetic field energy. Bottom: relative changes in the magnetic energy, internal energy, mass, and kinetic energy for N = 64. Solid lines denote a positive change in energy, while dashed lines denote a negative change.
Paper: GR-Athena++: General-relativistic Magnetohydrodynamics Simulations of Neutron Star Spacetimes
Authors: William Cook, Boris Daszuta, Jacob Fields, Peter Hammond, Simone Albanesi, Francesco Zappa, Sebastiano Bernuzzi, David Radice
Figure caption: Gravitational waveforms of collapsing D4 model. Data from PPM simulations are shown at all resolutions for the dominant (ℓ, m) = (2, 0) multipole of the strain. The waveform is shown as a function of the retardation time to the extraction sphere. At N = 32 the resolution is too low to observe convergence behavior, though at higher resolutions we do see convergence in the waveforms at approximate first order. Note the y-axis is plotted in geometric units.
Paper: GR-Athena++: General-relativistic Magnetohydrodynamics Simulations of Neutron Star Spacetimes
Authors: William Cook, Boris Daszuta, Jacob Fields, Peter Hammond, Simone Albanesi, Francesco Zappa, Sebastiano Bernuzzi, David Radice
Figure caption: Snapshot of Mesh structure at t = 7.88 ms shortly before merger in run AMRσ. Isocontours of density are also shown for ρ > 1011 g cm−3. For visualization purposes the z > 0,  y < 0 quadrant has been cut out.
Paper: An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Data collection pipeline. The figure shows the process for collecting SEP and non-SEP event data sets from the GSEP catalog, HEK website, and other sources. Three data modalities are included: M1 (tabular), M2 (EUV active region polygon), and M3 (GOES time-series proton flux).
Paper: An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Preprocessing pipeline for EUV image data from the SOHO 304 Å wavelength.
Paper: An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: GOES time-series proton flux channel P6.
Paper: An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Two-dimensional t-SNE data projection using only GOES time-series data (left) and using image and GOES time-series data modalities.
Paper: An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Architecture of the classifiers used in this study: (a) random forest, (b) support vector machine, (c) logistic regression, (d) XGBoost, (e) CatBoost, (f) multilayer perceptron, (g) time-series forest, and (h) ensemble.
Paper: The Dispersion Measure and Scattering of Fast Radio Bursts: Contributions from Multicomponents, and Clues for the Intrinsic Properties
Authors: Jian-feng Mo, Weishan Zhu, Long-Long Feng
Figure caption: The left panel shows the redshift distribution of our ∼106 mock FRB with fluence larger than 0.4 Jy ms, which are divided further into two populations. One is PSFR, i.e., tracing the cosmic star formation rate (red histogram), and the other is PStar, i.e., tracing the stellar mass (blue histogram). The red (blue) dotted line indicates the relative fraction, indicated by the right vertical axis, of mock FRBs that trace the PSFR (PStar) model in each redshift bin. In the right panel, the red solid (blue) line shows the fraction of mock PSFR (Pstar) FRBs that are hosted by star-forming galaxies as a function of redshift. The dashed line indicates the fraction of mock FRBs that are hosted by disk galaxies.
Paper: The Dispersion Measure and Scattering of Fast Radio Bursts: Contributions from Multicomponents, and Clues for the Intrinsic Properties
Authors: Jian-feng Mo, Weishan Zhu, Long-Long Feng
Figure caption: Similar to Figure 11, but for a larger τ range of 0.1–100 ms at 600 MHz. The title denotes the corresponding optimal parameters from the MCMC simulation with 20,000 steps.
Paper: The Dispersion Measure and Scattering of Fast Radio Bursts: Contributions from Multicomponents, and Clues for the Intrinsic Properties
Authors: Jian-feng Mo, Weishan Zhu, Long-Long Feng
Figure caption: Similar to Figure 15, but for “Z-zmock(DMonly)” (left) and “Z-DMIGM(DMonly)” (right) method. Top row: 69 FRB, excluding FRB 200120E and FRB 220319D. Bottom row: 68 FRB, excluding FRB 200120E, FRB 220319D, and FRB 190520B.
Paper: The Dispersion Measure and Scattering of Fast Radio Bursts: Contributions from Multicomponents, and Clues for the Intrinsic Properties
Authors: Jian-feng Mo, Weishan Zhu, Long-Long Feng
Figure caption: Similar to Figure 18, but using the following sSFR criterion: >10−10 yr−1.
Paper: The Dispersion Measure and Scattering of Fast Radio Bursts: Contributions from Multicomponents, and Clues for the Intrinsic Properties
Authors: Jian-feng Mo, Weishan Zhu, Long-Long Feng
Figure caption: The probability (top) and cumulative (bottom) distribution of DMHost (left), SMHost (middle), and τHost (right) based on TNG100 (blue) and TNG50 (green) simulation at z = 0 for PSFR source population model.
Paper: GR-Athena++: General-relativistic Magnetohydrodynamics Simulations of Neutron Star Spacetimes
Authors: William Cook, Boris Daszuta, Jacob Fields, Peter Hammond, Simone Albanesi, Francesco Zappa, Sebastiano Bernuzzi, David Radice
Figure caption: Top: amplification of total magnetic field strength ∣B 2∣ in a BNS merger for SMR with resolutions N = 64 and 128 and AMR criteria based on the maximum B field (AMRB) and velocity shear σ, (AMRσ). Middle: total number of MeshBlocks present in each run. Bottom: ∂ i  B  i  integrated over the computational domain, normalized by the maximum value of the magnetic field.
Paper: GR-Athena++: General-relativistic Magnetohydrodynamics Simulations of Neutron Star Spacetimes
Authors: William Cook, Boris Daszuta, Jacob Fields, Peter Hammond, Simone Albanesi, Francesco Zappa, Sebastiano Bernuzzi, David Radice
Figure caption: Scaling efficiency (see Equation (33)) for the BNS configuration with MHD. Results for both HAWK and SuperMUC are included, up to ﹩{ \mathcal O }({10}^{5})﹩ cores. Top: efficiency vs. number of cores. Middle: zone cycles per second (Z) vs. number of cores. Bottom: efficiency in terms of the MeshBlocks/core ratio.
Paper: GR-Athena++: General-relativistic Magnetohydrodynamics Simulations of Neutron Star Spacetimes
Authors: William Cook, Boris Daszuta, Jacob Fields, Peter Hammond, Simone Albanesi, Francesco Zappa, Sebastiano Bernuzzi, David Radice
Figure caption: Weak scaling (Equation (34)) for a single star and a BNS configuration (labeled NS and BNS, respectively) with and without magnetic fields (GRHD and GRMHD, respectively) for grids with SMR, comparing results on different target machines.
Paper: GR-Athena++: General-relativistic Magnetohydrodynamics Simulations of Neutron Star Spacetimes
Authors: William Cook, Boris Daszuta, Jacob Fields, Peter Hammond, Simone Albanesi, Francesco Zappa, Sebastiano Bernuzzi, David Radice
Figure caption: GRHD evolution of central rest-mass density of the static star A0 model. Top: varying reconstruction at constant resolution N = 64. Central: varying resolution for PPM reconstruction. Bottom: varying resolution for WENOZ reconstruction. Some data are cut to 5 ms for visualization purposes.
Paper: GR-Athena++: General-relativistic Magnetohydrodynamics Simulations of Neutron Star Spacetimes
Authors: William Cook, Boris Daszuta, Jacob Fields, Peter Hammond, Simone Albanesi, Francesco Zappa, Sebastiano Bernuzzi, David Radice
Figure caption:  L 1 norm on the x−y plane of the rest-mass density error of the static star model A0 for varying reconstruction. The dashed red line corresponds to the initial NS radius, while contour levels corresponds to rest-mass densities of ρ = [104, 106, 108, 1010, 1012, 1013, 5 × 1013, 1014, 5 × 1014] g cm−3. Data refer to GRHD evolutions at time t = 3.45 ms and at at constant resolution N = 64.
Paper: A Catalog of 12,766 Carbon-enhanced Metal-poor Stars from LAMOST Data Release 8
Authors: Ziyu Fang, Xiangru Li, Haining Li
Figure caption: The comparisons between this work and GALAH. The x-axis represents the parameter estimates from this work, while the y-axis represents the GALAH estimates. The color intensity corresponds to the density of samples, with brighter colors indicating higher sample densities. The black dashed line represents the line of theoretical consistency, while the red dashed line represents the 3σ reference line. The parameters μr and σr are the mean and standard deviation of the difference between two catalogs, respectively. The mean absolute difference between the two catalogs is shown. The horizontal coordinates of the graphs indicate the EGP values, the vertical coordinates indicate the density of the distribution, and the area of each bar graph sums to 1.
Paper: A Catalog of 12,766 Carbon-enhanced Metal-poor Stars from LAMOST Data Release 8
Authors: Ziyu Fang, Xiangru Li, Haining Li
Figure caption: Distribution of EGP indices for the CEMP stars, with blue for all CEMP stars searched and red for the CEMP stars in the training set.
Paper: A Catalog of 12,766 Carbon-enhanced Metal-poor Stars from LAMOST Data Release 8
Authors: Ziyu Fang, Xiangru Li, Haining Li
Figure caption: Dependences of the EGP index on [Fe/H] and [C/H] on the cases of 4300 K ≤ Teff ≤ 5000 K and ﹩{\rm{log}}\,g\leqslant 2.5﹩. The lines in (a), (b), and (c) are estimated using the samples in (c), similarly for the lines in (d), (e), and (f).
Paper: An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Sensitivity analysis showing the impact of noise on the performance of TS-HOG-TB and individual models. Note that the curves represent Gaussian kernel density estimates of accuracy values, which are smoothed representations of the data. While the kernel density estimate curves may extend slightly beyond the range of [0, 1] due to the smoothing process, all observed accuracy values remain within the valid range of [0, 1].
Paper: An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Performance of classifiers and feature extraction methods for EUV active region polygons.
Paper: An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Performance of classifiers for tabular data.
Paper: An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: SEP X-ray flux and flare class distribution, and their corresponding solar cycle counts.
Paper: An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: F1-scores for the TS-HOG-TB model across four EUV channels. The F1-scores are displayed for the 171, 195, 284, and 304 Å channels, corresponding to distinct layers and temperature regimes in the solar atmosphere.
Paper: An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Plots showing the accuracy and TSS of ensemble models. Dashed lines represent mean accuracies of unimodal models: TSF, RF, and SVM.
Paper: An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Performance of TS-HOG-TB (in terms of accuracy and TSS) on the train/test partitions.
Paper: An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: ML models’ performance when trained on time-series proton flux data from the GOES P6 channel.
Paper: An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Sequence analysis for TS-HOG-TB model.