Low Surface Brightness

Low surface brightness (LSB) galaxies are faint celestial objects posing significant challenges for observation and analysis due to their low light emission. Current research focuses on improving detection and characterization of LSB galaxies using advanced image processing techniques, including deep learning models like Mask R-CNN and vision-language models (VLMs), as well as Bayesian neural networks for parameter inference and uncertainty quantification. These efforts aim to enhance our understanding of galaxy formation and evolution by enabling more accurate measurements of structural parameters and improved image deconvolution, overcoming limitations of traditional methods. The resulting improvements in data analysis will significantly impact our knowledge of galaxy populations and the universe's large-scale structure.