Machine learning classifiers on initial orbital elements and convolutional neural networks on recurrence plots from short integrations classify long-term ejection of near-Earth asteroids with accuracy comparable to full numerical simulations.
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GalCatDiff applies category embeddings and a novel Astro-RAB block inside diffusion models to produce galaxy images whose color and size distributions match observations more closely than prior generative approaches.
A ResNet-34 classifier achieves 98.83% accuracy on six stellar density categories while a ResNet-50 regressor predicts bright-star counts with 0.0824 dex MAE for CSST image processing.
citing papers explorer
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Long-Term Dynamical Evolution and Ejection of Near-Earth Asteroids
Machine learning classifiers on initial orbital elements and convolutional neural networks on recurrence plots from short integrations classify long-term ejection of near-Earth asteroids with accuracy comparable to full numerical simulations.
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Category-based Galaxy Image Generation via Diffusion Models
GalCatDiff applies category embeddings and a novel Astro-RAB block inside diffusion models to produce galaxy images whose color and size distributions match observations more closely than prior generative approaches.
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Stellar Density Classification and Regression for CSST Multi-color Imaging Using Deep Learning
A ResNet-34 classifier achieves 98.83% accuracy on six stellar density categories while a ResNet-50 regressor predicts bright-star counts with 0.0824 dex MAE for CSST image processing.