arXiv Machine Learning September 10, 2026 By Luis Enrique Sucar, Carlos del Burgo, Jonathan Serrano-P\'erez

Deep learning from the crowd Fundamentals of morphological galaxy classification

Read the original on arXiv Machine Learning →

The study adapts a convolutional neural network to classify galaxy morphologies using crowd-sourced annotations from Galaxy Zoo 1. It evaluates how training strategies—such as training all layers versus only the last, incorporating hierarchical labels, varying data volume and annotator agreement, staged transfer learning, and ensembling—affect accuracy and efficiency. Results show that full-network training and high annotator agreement yield over 99% accuracy, while hierarchical approaches and staged learning help when data are limited.

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