Deep Learning for Protoplanetary Disk Classification from Hubble Images: A Comparative Study on Imbalanced Data - Fernandez, Marília M.; Levada, Alexandre L. M.; Bugatti, Pedro H. (Anais da Conference on Graphics, Patterns and Images (SIBGRAPI), 2025)
Protoplanetary disks (PPDs) are structures composed of gas and dust surrounding young stars, which play a fundamental role in understanding the formation of planetary systems. Despite their significance in astronomy, these objects remain underexplored in the image recognition literature. This study presents a comparative analysis of deep learning architectures for PPD classification, based on a catalog curated by domain experts. The dataset includes images captured by the Wide Field Channel of the Advanced Camera for Surveys (ACS/WFC) from the Hubble Space Telescope (HST) as part of the Orion Nebula Treasury program, along with annotations from the consolidated PPD catalog. To address class imbalance we rearrange the original classes and use data augmentation techniques of geometric image transformations and band combination. The experimental setup explores a range of neural network architectures and hyperparameters, including traditional models, such as VGG, ResNet, Inception and EfficientNet, as well as a more recent ConvNeXt architecture. An ablation study is also conducted to assess the contribution of NLM+Anscombe as a denoising step. The results indicate a general difficulty in learning discriminative patterns from the data. Among the evaluated architectures, EfficientNet achieved the best overall performance, with a mean F1-score of 53%, reaching a maximum of 65% using a learning rate of 0.02 and a weight decay of 0.0004. The denoising filtering showed no statistically significant impact on model performance for the specific configuration.