no code implementations • 25 Sep 2023 • Chetraj Pandey, Rafal A. Angryk, Berkay Aydin
We trained three well-known deep learning architectures--AlexNet, VGG16, and ResNet34 using transfer learning and compared and evaluated the overall performance of our models using true skill statistics (TSS) and Heidke skill score (HSS) and computed recall scores to understand the prediction sensitivity in central and near-limb regions for both X- and M-class flares.
1 code implementation • 8 Sep 2023 • Chetraj Pandey, Anli Ji, Rafal A. Angryk, Berkay Aydin
In this work, we developed an attention-based deep learning model as an improvement over the standard convolutional neural network (CNN) pipeline to perform full-disk binary flare predictions for the occurrence of $\geq$M1. 0-class flares within the next 24 hours.
1 code implementation • 30 Aug 2023 • Chetraj Pandey, Anli Ji, Trisha Nandakumar, Rafal A. Angryk, Berkay Aydin
This study progresses solar flare prediction research by presenting a full-disk deep-learning model to forecast $\geq$M-class solar flares and evaluating its efficacy on both central (within $\pm$70$^\circ$) and near-limb (beyond $\pm$70$^\circ$) events, showcasing qualitative assessment of post hoc explanations for the model's predictions, and providing empirical findings from human-centered quantitative assessments of these explanations.
1 code implementation • 4 Aug 2023 • Chetraj Pandey, Rafal A. Angryk, Manolis K. Georgoulis, Berkay Aydin
This paper presents a post hoc analysis of a deep learning-based full-disk solar flare prediction model.
1 code implementation • 29 Jul 2023 • Chetraj Pandey, Rafal A. Angryk, Berkay Aydin
This paper contributes to the growing body of research on deep learning methods for solar flare prediction, primarily focusing on highly overlooked near-limb flares and utilizing the attribution methods to provide a post hoc qualitative explanation of the model's predictions.
1 code implementation • 11 Aug 2022 • Chetraj Pandey, Anli Ji, Rafal A. Angryk, Manolis K. Georgoulis, Berkay Aydin
We utilized an equal weighted average ensemble of two base learners' flare probabilities as our baseline meta learner and improved the capabilities of our two base learners by training a logistic regression model.