{"ID":2868243,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18214","arxiv_id":"2509.18214","title":"Automatic Classification of Magnetic Chirality of Solar Filaments from H-Alpha Observations","abstract":"In this study, we classify the magnetic chirality of solar filaments from H-Alpha observations using state-of-the-art image classification models. We establish the first reproducible baseline for solar filament chirality classification on the MAGFiLO dataset. The MAGFiLO dataset contains over 10,000 manually-annotated filaments from GONG H-Alpha observations, making it the largest dataset for filament detection and classification to date. Prior studies relied on much smaller datasets, which limited their generalizability and comparability. We fine-tuned several pre-trained, image classification architectures, including ResNet, WideResNet, ResNeXt, and ConvNeXt, and also applied data augmentation and per-class loss weights to optimize the models. Our best model, ConvNeXtBase, achieves a per-class accuracy of 0.69 for left chirality filaments and $0.73$ for right chirality filaments.","short_abstract":"In this study, we classify the magnetic chirality of solar filaments from H-Alpha observations using state-of-the-art image classification models. We establish the first reproducible baseline for solar filament chirality classification on the MAGFiLO dataset. The MAGFiLO dataset contains over 10,000 manually-annotated...","url_abs":"https://arxiv.org/abs/2509.18214","url_pdf":"https://arxiv.org/pdf/2509.18214v1","authors":"[\"Alexis Chalmers\",\"Azim Ahmadzadeh\"]","published":"2025-09-21T19:55:14Z","proceeding":"astro-ph.SR","tasks":"[\"astro-ph.SR\",\"cs.AI\"]","methods":"[]","has_code":false}