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.
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Mock CSST images yield 95% completeness limits of 26.3-28.5 mag for point sources and 24.4-27.1 mag for galaxies, with fainter objects showing systematic overestimates in magnitude, size, and surface brightness and underestimates in Sersic index and axis ratio.
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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.
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CSST Preparations: Galaxy Completeness and S\'ersic Profile Fitting across the Wide, Deep, and Extreme Fields
Mock CSST images yield 95% completeness limits of 26.3-28.5 mag for point sources and 24.4-27.1 mag for galaxies, with fainter objects showing systematic overestimates in magnitude, size, and surface brightness and underestimates in Sersic index and axis ratio.