adsabs/AstroCLIMB huggingface dataset:
Paper: Evaluating Time-series Augmentation Techniques for Deep Learning–Based Solar Flare Prediction
Authors: Peiyu Li, Omar Bahri, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: The pipeline of the data augmentation.
Paper: Solar Flare Forecasting Using Machine Learning and SDO/HMI Data: A Multiple Machine Learning Model and Data Curation Technique Comparison Study
Authors: Timothy S. Newman, Christian W. Hall, Luke Farris, Talwinder Singh, Nikolai Pogorelov, Bernard Benson, Syed Raza, Prajun Trital
Figure caption: SVM approach hyperparameter combinations and associated TSS (color coded), on data 24 hr prior to flare onset.
Paper: Solar Flare Forecasting Using Machine Learning and SDO/HMI Data: A Multiple Machine Learning Model and Data Curation Technique Comparison Study
Authors: Timothy S. Newman, Christian W. Hall, Luke Farris, Talwinder Singh, Nikolai Pogorelov, Bernard Benson, Syed Raza, Prajun Trital
Figure caption: LR approach hyperparameter choice and associated TSS, on data 24 hr prior to flare onset.
Paper: Solar Flare Forecasting Using Machine Learning and SDO/HMI Data: A Multiple Machine Learning Model and Data Curation Technique Comparison Study
Authors: Timothy S. Newman, Christian W. Hall, Luke Farris, Talwinder Singh, Nikolai Pogorelov, Bernard Benson, Syed Raza, Prajun Trital
Figure caption: RFC approach hyperparameter choice and associated TSS.
Paper: Solar Flare Forecasting Using Machine Learning and SDO/HMI Data: A Multiple Machine Learning Model and Data Curation Technique Comparison Study
Authors: Timothy S. Newman, Christian W. Hall, Luke Farris, Talwinder Singh, Nikolai Pogorelov, Bernard Benson, Syed Raza, Prajun Trital
Figure caption: TSS using the ML algorithms considered in this study. The TSS values are calculated for lead times between 24 and 0 hr before flare onset. The TSS results are shown without consideration of AR flaring history (top panel) vs. considering AR flaring history (middle panel). The bottom panel shows the percentage improvement in TSS when using flare history parameters in our analysis.
Paper: Solar Flare Forecasting Using Machine Learning and SDO/HMI Data: A Multiple Machine Learning Model and Data Curation Technique Comparison Study
Authors: Timothy S. Newman, Christian W. Hall, Luke Farris, Talwinder Singh, Nikolai Pogorelov, Bernard Benson, Syed Raza, Prajun Trital
Figure caption: TSS metrics for univariate (top panel) and multivariate (bottom panel) classification using logistic regression on the SHARP parameters 24 hr prior to flare onset.
Paper: Solar Flare Forecasting Using Machine Learning and SDO/HMI Data: A Multiple Machine Learning Model and Data Curation Technique Comparison Study
Authors: Timothy S. Newman, Christian W. Hall, Luke Farris, Talwinder Singh, Nikolai Pogorelov, Bernard Benson, Syed Raza, Prajun Trital
Figure caption: Pearson’s correlation coefficients (color coded) for the time-averaged SHARP parameters over a period of 0–24 hr prior to flare onset. This was done for 20 parameters across all SHARPs in our database.
Paper: Solar Flare Forecasting Using Machine Learning and SDO/HMI Data: A Multiple Machine Learning Model and Data Curation Technique Comparison Study
Authors: Timothy S. Newman, Christian W. Hall, Luke Farris, Talwinder Singh, Nikolai Pogorelov, Bernard Benson, Syed Raza, Prajun Trital
Figure caption: KNN approach hyperparameter choice and associated TSS.
Paper: COSINE (Cometary Object Study Investigating Their Nature and Evolution). I. Project Overview and General Characteristics of Detected Comets
Authors: Yuna G. Kwon, Dar W. Dahlen, Joseph R. Masiero, James M. Bauer, Yanga R. Fernández, Adeline Gicquel, Yoonyoung Kim, Jana Pittichová, Frank Masci, Roc M. Cutri, Amy K. Mainzer
Figure caption: Example of comets classified as Y-A (top two rows) and N-A (bottom two rows). Columns, from left to right, show (1) the coadded image with vector overlays; (2) the azimuthal signal distribution, sampled in 1° intervals, along the radial distance from the photocenter; and (3) the radial S/N profile along the primary PA, marked in the middle panel by a vertical dashed line. In the first column, vectors denote the antisolar vector (red), best PA (salmon), and negative velocity vector (yellow). In the second column, gray and black contours indicate S/N levels in intervals of one (for S/N < 10) and 10 (for S/N > 10), respectively. In the third column, Gaussian profiles with standard deviations of 2.4, 3.0, and 4.0 pixels (6﹩\mathop{.}\limits^{{\prime\prime} }﹩ 6, 8﹩\mathop{.}\limits^{{\prime\prime} }﹩ 25, and 11﹩\mathop{.}\limits^{{\prime\prime} }﹩ 0, respectively) are overplotted as solid, dashed, and dotted lines, respectively, with different colors representing individual bands.
Paper: COSINE (Cometary Object Study Investigating Their Nature and Evolution). I. Project Overview and General Characteristics of Detected Comets
Authors: Yuna G. Kwon, Dar W. Dahlen, Joseph R. Masiero, James M. Bauer, Yanga R. Fernández, Adeline Gicquel, Yoonyoung Kim, Jana Pittichová, Frank Masci, Roc M. Cutri, Amy K. Mainzer
Figure caption: Two-stage filtering applied to the 3768 known comets (as of 2024 October). Left: a heliocentric distance cutoff of 11.5 au (Section 2.1) yields 1335 queryable comets. Right: imposing a signal-to-noise ratio (S/N) threshold of S/N > 4 on the stacked frames identifies 484 detected comets across 1633 frames from 966 epochs. These detections constitute the primary data set analyzed in this study (Section 2.3). In the right panel, comets with stacked S/N > 400 are grouped in the highest bin.
Paper: COSINE (Cometary Object Study Investigating Their Nature and Evolution). I. Project Overview and General Characteristics of Detected Comets
Authors: Yuna G. Kwon, Dar W. Dahlen, Joseph R. Masiero, James M. Bauer, Yanga R. Fernández, Adeline Gicquel, Yoonyoung Kim, Jana Pittichová, Frank Masci, Roc M. Cutri, Amy K. Mainzer
Figure caption: False-color image example of detected comets from the Cryo phase. The W2, W3, and W4 bands are mapped to blue, green, and red, respectively. Pixel values within the [10, 99.999] percentile were normalized to [0, 1] to suppress dominant background features, then rescaled to the [0.1, 99.9] range for visualization. Each panel includes the comet’s heliocentric distance (rH, in au units) at the time of observation, along with orientation vectors: ecliptic north (“N”), ecliptic east (“E”), the negative velocity vector (−v), and the antisolar vector (r⊙). A 1′ scale bar and orbital true anomaly diagram are also included.
Paper: COSINE (Cometary Object Study Investigating Their Nature and Evolution). I. Project Overview and General Characteristics of Detected Comets
Authors: Yuna G. Kwon, Dar W. Dahlen, Joseph R. Masiero, James M. Bauer, Yanga R. Fernández, Adeline Gicquel, Yoonyoung Kim, Jana Pittichová, Frank Masci, Roc M. Cutri, Amy K. Mainzer
Figure caption: Timeline of the WISE/NEOWISE mission phases. Cryo: UT 2010 January 14 to UT 2010 August 6 (bands: W1, W2, W3, and W4); 3-Band: UT 2010 August 6 to UT 2010 September 29 (bands: W1, W2, and W3); Post-Cryo: UT 2010 September 29 to 2011 February 1 (bands: W1 and W2); and Reactivation: UT 2013 December 13 to UT 2024 August 1 (bands: W1 and W2).
Paper: COSINE (Cometary Object Study Investigating Their Nature and Evolution). I. Project Overview and General Characteristics of Detected Comets
Authors: Yuna G. Kwon, Dar W. Dahlen, Joseph R. Masiero, James M. Bauer, Yanga R. Fernández, Adeline Gicquel, Yoonyoung Kim, Jana Pittichová, Frank Masci, Roc M. Cutri, Amy K. Mainzer
Figure caption: Same as Figure 5, but for comets classified as Y-C (top two rows) and N-C (bottom two rows).
Paper: COSINE (Cometary Object Study Investigating Their Nature and Evolution). I. Project Overview and General Characteristics of Detected Comets
Authors: Yuna G. Kwon, Dar W. Dahlen, Joseph R. Masiero, James M. Bauer, Yanga R. Fernández, Adeline Gicquel, Yoonyoung Kim, Jana Pittichová, Frank Masci, Roc M. Cutri, Amy K. Mainzer
Figure caption: Same as Figure 5, but for comets classified as Y-B (top two rows) and N-B (bottom two rows).
Paper: COSINE (Cometary Object Study Investigating Their Nature and Evolution). I. Project Overview and General Characteristics of Detected Comets
Authors: Yuna G. Kwon, Dar W. Dahlen, Joseph R. Masiero, James M. Bauer, Yanga R. Fernández, Adeline Gicquel, Yoonyoung Kim, Jana Pittichová, Frank Masci, Roc M. Cutri, Amy K. Mainzer
Figure caption: Flowchart summarizing the WISE/NEOWISE data analysis procedure used in this study. Each highlighted module corresponds to detailed methods described in the following sections. Photometric results are based on the combined signal from the nucleus and coma.
Paper: COSINE (Cometary Object Study Investigating Their Nature and Evolution). I. Project Overview and General Characteristics of Detected Comets
Authors: Yuna G. Kwon, Dar W. Dahlen, Joseph R. Masiero, James M. Bauer, Yanga R. Fernández, Adeline Gicquel, Yoonyoung Kim, Jana Pittichová, Frank Masci, Roc M. Cutri, Amy K. Mainzer
Figure caption: False-color composite images of 178 comets observed during the Cryo phase. This sample does not represent the complete Cryo-phase data set. The W2, W3, and W4 bands are mapped to blue, green, and red, respectively, with scaling and normalization consistent with Figure 4. Each panel includes the comet’s heliocentric distance (rH in au units) at the time of observation, along with directional vectors indicating ecliptic north (“N”), ecliptic east (“E”), the negative velocity vector (−v), and the antisolar vector (r⊙). A 1′ scale bar is also provided. The object’s position in its orbit, given by the true anomaly, is illustrated in the inset diagram at the upper right.
Paper: COSINE (Cometary Object Study Investigating Their Nature and Evolution). I. Project Overview and General Characteristics of Detected Comets
Authors: Yuna G. Kwon, Dar W. Dahlen, Joseph R. Masiero, James M. Bauer, Yanga R. Fernández, Adeline Gicquel, Yoonyoung Kim, Jana Pittichová, Frank Masci, Roc M. Cutri, Amy K. Mainzer
Figure caption: Example of comet 223P/Skiff demonstrating the internal consistency of our stacking technique and photometry. Left: coadded W3-band image from 11 frames taken on MJD 55417.23328 (Cryo phase) at rH = 2.420 au and Δ = 2.186 au. Right: radial S/N profiles for the 11 individual frames (black curves) and the final coadded image (blue curve). Note that the second y-axis (blue) is scaled by exactly ﹩\sqrt{11}﹩, the number of individual frames used to generate the coadd.
Paper: COSINE (Cometary Object Study Investigating Their Nature and Evolution). I. Project Overview and General Characteristics of Detected Comets
Authors: Yuna G. Kwon, Dar W. Dahlen, Joseph R. Masiero, James M. Bauer, Yanga R. Fernández, Adeline Gicquel, Yoonyoung Kim, Jana Pittichová, Frank Masci, Roc M. Cutri, Amy K. Mainzer
Figure caption: Number of epochs as a function of estimated V-band magnitude for the comets shown in Figure 10, using the same color scheme.
Paper: COSINE (Cometary Object Study Investigating Their Nature and Evolution). I. Project Overview and General Characteristics of Detected Comets
Authors: Yuna G. Kwon, Dar W. Dahlen, Joseph R. Masiero, James M. Bauer, Yanga R. Fernández, Adeline Gicquel, Yoonyoung Kim, Jana Pittichová, Frank Masci, Roc M. Cutri, Amy K. Mainzer
Figure caption: Number of detected comets per year. Complete comet profiles are provided in Table B1.
Paper: COSINE (Cometary Object Study Investigating Their Nature and Evolution). I. Project Overview and General Characteristics of Detected Comets
Authors: Yuna G. Kwon, Dar W. Dahlen, Joseph R. Masiero, James M. Bauer, Yanga R. Fernández, Adeline Gicquel, Yoonyoung Kim, Jana Pittichová, Frank Masci, Roc M. Cutri, Amy K. Mainzer
Figure caption: Phase-angle distribution of 1633 comet detection frames (circle symbols) from WISE/NEOWISE. The purple-shaded region indicates the geometrically allowed range of phase angles at a given heliocentric distance (rH), assuming the comet is located at that rH. Owing to WISE/NEOWISE’s near-Earth orbit and its fixed ∼90° solar elongation constraint, the mission preferentially sampled the upper end of the available phase-angle range.
Paper: COSINE (Cometary Object Study Investigating Their Nature and Evolution). I. Project Overview and General Characteristics of Detected Comets
Authors: Yuna G. Kwon, Dar W. Dahlen, Joseph R. Masiero, James M. Bauer, Yanga R. Fernández, Adeline Gicquel, Yoonyoung Kim, Jana Pittichová, Frank Masci, Roc M. Cutri, Amy K. Mainzer
Figure caption: Filter transmission curves for the W1 and W2 bands from the WISE Explanatory Supplement, overlaid with the mean central wavelengths of prominent cometary gas emission features (N. Dello Russo et al. 2011; W. T. Reach et al. 2013; L. Paganini et al. 2015): 1—H2O at ∼2.94 μm; 2—HCN at ∼3.03 μm; 3—CH4 at ∼3.32 μm; 4—C2H6ν7 at ∼3.35 μm; 5—C2H6ν5 at ∼3.45 μm); 6—CH3OH at ∼3.48 μm; 7—CO2 at ∼4.3 μm; and 8—CO at ∼4.7 μm.
Paper: COSINE (Cometary Object Study Investigating Their Nature and Evolution). I. Project Overview and General Characteristics of Detected Comets
Authors: Yuna G. Kwon, Dar W. Dahlen, Joseph R. Masiero, James M. Bauer, Yanga R. Fernández, Adeline Gicquel, Yoonyoung Kim, Jana Pittichová, Frank Masci, Roc M. Cutri, Amy K. Mainzer
Figure caption: Distributions of inactive comets in all four bands as a function of phase angle. The data points represent m(1, 1, α) values derived per frame, and S/N = 4 trend lines are overlaid for each band.
Paper: Radio Afterglow Detection and AI-driven Response (RADAR): A Federated Framework for Gravitational-wave Event Follow-up
Authors: Parth Patel, Alessandra Corsi, E. A. Huerta, Kara Merfeld, Victoria Tiki, Zilinghan Li, Tekin Bicer, Kyle Chard, Ryan Chard, Ian T. Foster, Maxime Gonthier, Valerie Hayot-Sasson, Hai Duc Nguyen, Haochen Pan
Figure caption: Schematic overview of RADAR, our federated, AI-driven computational framework for MMA. RADAR consists of four interconnected modules that collectively enable detection, federated inference, and joint scientific interpretation of GW and EM data. For this demonstration, rather than interfacing directly with the LIGO Hanford and Livingston detectors, we emulate detector endpoints by hosting their respective data streams on two distinct HPC platforms—the DeltaAI supercomputer at the NCSA and the Polaris supercomputer at the ALCF. (1) The GW module (left) deploys AI models at each site, DeltaAI and Polaris, for prompt signal detection and latent embedding generation. Embeddings are then transmitted to a central server hosted on the Delta supercomputer at NCSA for postprocessing and candidate event identification. In the graph at the bottom left of the box, we show GW data containing a signal (“ground truth”) correctly identified by the AI model. The corresponding “prediction” is indicated by the line that spikes to 1 when the signal is detected and returns to 0 otherwise. See also Figure 2 and Appendix A for more details. The radio module (right) performs federated light-curve modeling using afterglowpy, aggregating radio follow-up data from both public GCN circulars and private observatories. A dedicated GCN listener ingests new notices in real time, while an AI GCN parser, based on the GPT-4.1 LLM, extracts structured metadata and flags incomplete entries to ensure robust downstream analysis. (3) The MMA module (bottom) performs posterior consistency checks by comparing overlapping parameters (e.g., luminosity distance DL and viewing angle θobs) inferred from GW data, through posterior sampling via the Dingo-BNS inference engine, and EM channels, through posterior sampling via afterglowpy, leveraging federated posteriors published by upstream modules. (4) The Octopus event fabric (center) acts as a messaging and coordination layer, enabling asynchronous communication of posterior samples and intermediate results from producers to consumers for progressive inference. These mechanisms allow the overall architecture to support data locality, minimize communication overhead, track provenance, and achieve privacy-enhancing collaborative analysis across heterogeneous facilities.
Paper: Radio Afterglow Detection and AI-driven Response (RADAR): A Federated Framework for Gravitational-wave Event Follow-up
Authors: Parth Patel, Alessandra Corsi, E. A. Huerta, Kara Merfeld, Victoria Tiki, Zilinghan Li, Tekin Bicer, Kyle Chard, Ryan Chard, Ian T. Foster, Maxime Gonthier, Valerie Hayot-Sasson, Hai Duc Nguyen, Haochen Pan
Figure caption: GW170817 case study: posterior distributions for the observing angle (θobs) and luminosity distance (DL) derived using Dingo-BNS and our federated radio fitting approach (Section 6) are compared in the left and right panels. Contours indicate 68% (solid) and 95% (dashed) confidence intervals. These results are obtained by using the Dingo-BNS-derived GW 68% posteriors as priors for the radio data modeled using afterglowpy. The left panel incorporates the radio fit of all observations collected up to 60 days postmerger. The right panel incorporates all radio follow-up observations of GW170817 presented in S. Makhathini et al. (2021). See Section 7 for a discussion.
Paper: Radio Afterglow Detection and AI-driven Response (RADAR): A Federated Framework for Gravitational-wave Event Follow-up
Authors: Parth Patel, Alessandra Corsi, E. A. Huerta, Kara Merfeld, Victoria Tiki, Zilinghan Li, Tekin Bicer, Kyle Chard, Ryan Chard, Ian T. Foster, Maxime Gonthier, Valerie Hayot-Sasson, Hai Duc Nguyen, Haochen Pan
Figure caption: Federated radio light-curve results for the GW170817 case study. This fit is obtained by using the GW constraints (68% Dingo-BNS posterior) on the DL and θobs parameters of afterglowpy (see Table 1) and a federated fitting strategy simulating eight geographically distributed private sites contributing radio observations of GW170817 (see Table 2). For completeness, we also show the upper limits on the radio flux density of GW170817, corresponding to nondetections, as downward-pointing triangles. These upper limits have been extracted and aggregated from GCNs using the AI-powered GCN parser developed as part of this study. They are not considered for the fit.
Paper: Radio Afterglow Detection and AI-driven Response (RADAR): A Federated Framework for Gravitational-wave Event Follow-up
Authors: Parth Patel, Alessandra Corsi, E. A. Huerta, Kara Merfeld, Victoria Tiki, Zilinghan Li, Tekin Bicer, Kyle Chard, Ryan Chard, Ian T. Foster, Maxime Gonthier, Valerie Hayot-Sasson, Hai Duc Nguyen, Haochen Pan
Figure caption: Whitened noisy GW signal for Livingston (L) and Hanford (H) signals. For training the AI model used for GW detection, each waveform is labeled “1” in the 0.5 s leading up to the merger event and “0” elsewhere. See the text for discussion.
Paper: Radio Afterglow Detection and AI-driven Response (RADAR): A Federated Framework for Gravitational-wave Event Follow-up
Authors: Parth Patel, Alessandra Corsi, E. A. Huerta, Kara Merfeld, Victoria Tiki, Zilinghan Li, Tekin Bicer, Kyle Chard, Ryan Chard, Ian T. Foster, Maxime Gonthier, Valerie Hayot-Sasson, Hai Duc Nguyen, Haochen Pan
Figure caption: Posterior distributions for the observing angle (θobs) and luminosity distance (DL) derived using Dingo-BNS and our federated radio fitting approach, when, as per resilience scenario (3) in the text, the site with the most data points (site 3 in Table 2) is omitted. The procedure for federated fitting used here is as in Figure 4.
Paper: Radio Afterglow Detection and AI-driven Response (RADAR): A Federated Framework for Gravitational-wave Event Follow-up
Authors: Parth Patel, Alessandra Corsi, E. A. Huerta, Kara Merfeld, Victoria Tiki, Zilinghan Li, Tekin Bicer, Kyle Chard, Ryan Chard, Ian T. Foster, Maxime Gonthier, Valerie Hayot-Sasson, Hai Duc Nguyen, Haochen Pan
Figure caption: HDCN submodule for the Livingston strain channel. An identical module is used for the Hanford strain channel. Each block consists of dilated convolutions with skip connections and nonlinear gating.
Paper: Radio Afterglow Detection and AI-driven Response (RADAR): A Federated Framework for Gravitational-wave Event Follow-up
Authors: Parth Patel, Alessandra Corsi, E. A. Huerta, Kara Merfeld, Victoria Tiki, Zilinghan Li, Tekin Bicer, Kyle Chard, Ryan Chard, Ian T. Foster, Maxime Gonthier, Valerie Hayot-Sasson, Hai Duc Nguyen, Haochen Pan
Figure caption: CAN submodules. Top: Hanford features attending to Livingston. Bottom: Livingston features attending to Hanford. Each module projects features to query, key, and value spaces before performing multihead attention and output projection.
Paper: Radio Afterglow Detection and AI-driven Response (RADAR): A Federated Framework for Gravitational-wave Event Follow-up
Authors: Parth Patel, Alessandra Corsi, E. A. Huerta, Kara Merfeld, Victoria Tiki, Zilinghan Li, Tekin Bicer, Kyle Chard, Ryan Chard, Ian T. Foster, Maxime Gonthier, Valerie Hayot-Sasson, Hai Duc Nguyen, Haochen Pan
Figure caption: Final output module. The enriched features from both detectors are concatenated, pooled, and projected to generate the per time step output score.
Paper: Evaluating Time-series Augmentation Techniques for Deep Learning–Based Solar Flare Prediction
Authors: Peiyu Li, Omar Bahri, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: The class distribution and the 2D PCA projections of the original data set.
Paper: Evaluating Time-series Augmentation Techniques for Deep Learning–Based Solar Flare Prediction
Authors: Peiyu Li, Omar Bahri, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Real sample and synthetic sample comparison among different augmentation methods.
Paper: Evaluating Time-series Augmentation Techniques for Deep Learning–Based Solar Flare Prediction
Authors: Peiyu Li, Omar Bahri, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: UMAP visualization of the real samples and the synthetic samples generated by each augmentation method.
Paper: Evaluating Time-series Augmentation Techniques for Deep Learning–Based Solar Flare Prediction
Authors: Peiyu Li, Omar Bahri, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: t-SNE visualization of the real samples and the synthetic samples generated by each augmentation method.
Paper: Evaluating Time-series Augmentation Techniques for Deep Learning–Based Solar Flare Prediction
Authors: Peiyu Li, Omar Bahri, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: PCA visualization of the real samples and the synthetic samples generated by each augmentation method.
Paper: Evaluating Time-series Augmentation Techniques for Deep Learning–Based Solar Flare Prediction
Authors: Peiyu Li, Omar Bahri, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Confusion matrices for the MLSTM-FCN model comparing the baseline (stratified sampling) with three augmentation methods (SPAWNER, RGW, and DGW).
Paper: Evaluating Time-series Augmentation Techniques for Deep Learning–Based Solar Flare Prediction
Authors: Peiyu Li, Omar Bahri, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Confusion matrices for the ResNet model comparing the baseline (stratified sampling) with three augmentation methods (PM, WS, and WW).
Paper: Evaluating Time-series Augmentation Techniques for Deep Learning–Based Solar Flare Prediction
Authors: Peiyu Li, Omar Bahri, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Performance comparison with different synthetic/real ratios (ResNet model with permutation augmentation method).
Paper: Evaluating Time-series Augmentation Techniques for Deep Learning–Based Solar Flare Prediction
Authors: Peiyu Li, Omar Bahri, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Performance comparison with different synthetic/real ratios (MLSTM-FCN model with SPAWNER augmentation method).
Paper: Evaluating Time-series Augmentation Techniques for Deep Learning–Based Solar Flare Prediction
Authors: Peiyu Li, Omar Bahri, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Performance comparison with different synthetic/real ratios (FCN model with permutation augmentation method).