Full Redshift Probability Distribution

Determining the full redshift probability distribution of astronomical objects is crucial for accurate cosmological analyses, particularly in large-scale surveys where spectroscopic redshifts are limited. Current research focuses on developing and refining machine learning models, including convolutional neural networks and graph neural networks, to predict these distributions from photometric data and other features like galaxy images and light curves, aiming to improve accuracy and reduce biases. These advancements are vital for maximizing the scientific return of upcoming large-scale surveys by enabling more precise measurements of cosmological parameters and a deeper understanding of galaxy evolution.