Large Scale Structure

Large-scale structure (LSS) research focuses on understanding the distribution of matter in the universe, primarily to constrain cosmological parameters and probe dark energy. Current efforts leverage machine learning, employing architectures like variational autoencoders, generative adversarial networks, and denoising diffusion models, to analyze simulation data, improve the resolution of simulations, and extract cosmological information from complex datasets such as matter power spectra and galaxy clustering. These advancements are crucial for maximizing the scientific return from current and future large-scale surveys, improving our understanding of fundamental physics and the universe's evolution.