Photometric Redshift

Photometric redshift estimation aims to determine the distance to galaxies using only their observed brightness in different wavelengths, a crucial task for cosmology and large-scale surveys. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), vision transformers, and masked autoencoders to analyze galaxy images and photometry, often incorporating multi-modal data from various telescopes. These advancements improve accuracy and efficiency compared to traditional methods, mitigating biases and enabling more precise measurements of galaxy properties, ultimately enhancing our understanding of the universe's structure and evolution.