Classificação morfológica de galáxias com Deep Learning nos Surveys SDSS e S-PLUS

Abstract

Morphological classification of galaxies in large photometric surveys is central to galaxy evolution studies, but it remains challenging when deep learning models are transferred across different data domains. This dissertation addresses the problem by developing and evaluating deep learning models for binary galaxy classification (regular vs. peculiar), with emphasis on quantifying the domain shift between the Sloan Digital Sky Survey (SDSS) and the Southern Photometric Local Universe Survey (S-PLUS). Using transfer learning, a benchmark of 11 pre-trained architectures was conducted, including EfficientNet (B0-B4), ResNet/ResNeXt, and Vision Transformer (ViT-B/16 and ViT-B/32), plus a simple CNN trained from scratch. The models were trained and validated on a curated dataset of 11,662 galaxies with images from SDSS and S-PLUS, labeled from the RC3 catalog. In the SDSS domain, the best model was ViT-B/32, with an F1-score of 0.923 and an AUC of 0.963, followed by EfficientNet-B4 (F1-score 0.910) and ViT-B/16 (F1-score 0.899). The superiority of attention-based models suggests that long-range spatial modeling is advantageous for this task. In contrast, the simple CNN showed the worst performance (F1-score 0.655), reinforcing the importance of transfer learning. Evaluation of the best model on S-PLUS quantifies the performance degradation, with a drop of 14.8 percentage points in F1-score (from 0.923 to 0.775), confirming a significant domain shift effect. The Peculiar class is the most affected, with a 29.8% drop in recall, while average predictive uncertainty increases by 65.4% and calibration worsens by more than 50%. This work contributes a comparative architecture benchmark, a curated dataset, and a quantitative analysis of cross-survey generalization, showing that domain adaptation is an indispensable methodological step for the practical use of these models in different observational contexts.