Structural and Photometric Parameter Estimation of Low-surface-brightness Galaxies Using a Deep Learning Framework

Abstract

Low-surface-brightness galaxies (LSBGs) play an important role in studies of galaxy formation and evolution, yet accurate measurements of their structural and photometric parameters remain challenging for conventional analysis pipelines due to their diffuse light distributions and low signal-to-noise ratios. In this work, we present an automated deep learning framework, LSBGPENet, for the robust estimation of structural and photometric parameters of LSBGs from wide-field imaging data. The framework directly operates on galaxy image cutouts and simultaneously infers key parameters, including total magnitude (m), effective radius (Reff), ellipticity (ϵ), and Sérsic index (n), together with associated uncertainty estimates. Mean and central surface brightnesses (μeff, μ0) are subsequently derived from the inferred parameters. We assess the accuracy and reliability of the inferred parameters through comparisons with traditional profile-fitting measurements. On both simulated data and observational data from the Dark Energy Survey, the framework achieves high predictive accuracy, with mean coefficients of determination of 0.91 and 0.94, respectively, and well-calibrated uncertainty estimates, characterized by mean uncertainty calibration errors of 0.004 and 0.009. The inferred parameters are statistically consistent with those obtained from GALFIT. The proposed framework provides a scalable and reproducible solution for structural and photometric parameter estimation of LSBGs and is well suited for application to current and forthcoming wide-field surveys, including the China Space Station Telescope.