Structural and Photometric Parameter Estimation of Low-surface-brightness Galaxies Using a Deep Learning Framework
This work presents an automated deep learning framework, LSBGPENet, for the robust estimation of structural and photometric parameters of LSBGs from wide-field imaging data and assesses the accuracy and reliability of the inferred parameters through comparisons with traditional profile-fitting measurements.