Summary of Embedding Ordinality to Binary Loss Function For Improving Solar Flare Forecasting, by Chetraj Pandey et al.
Embedding Ordinality to Binary Loss Function for Improving Solar Flare Forecasting
by Chetraj Pandey, Anli Ji, Jinsu Hong, Rafal A. Angryk, Berkay Aydin
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Instrumentation and Methods for Astrophysics (astro-ph.IM); Solar and Stellar Astrophysics (astro-ph.SR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel loss function optimizes binary flare prediction by embedding ordinal characteristics into the binary cross-entropy (BCE) loss function. A ResNet34-based model with transfer learning is employed for predicting M-class flares using magnetogram features of active region patches. The composite skill score (CSS), calculated as the geometric mean of True Skill Score (TSS) and Heidke Skill Score (HSS), evaluates the performance of the models. The paper introduces a novel approach to encoding ordinality into a binary loss function, enhances solar flare forecasting by predicting flares for each active region across the entire solar disk, and demonstrates an improvement in performance with the proposed loss function. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to predict solar flares by using special mathematical functions. This helps improve the accuracy of predictions. The researchers used a specific type of model called ResNet34, which was trained on data from magnetograms. These magnetograms are like pictures of the sun’s magnetic field. The team also created a special way to measure how well their model did, which they called the composite skill score. This helps compare their results with others who might be working on similar problems. |
Keywords
» Artificial intelligence » Cross entropy » Embedding » Loss function » Transfer learning
