Poster
2025 workshop program
AI-Driven Methods for Identifying Cosmic Web Environments
The work
Abstract
Identifying large-scale cosmic web structures in observational data remains challenging, particularly at high redshifts where traditional physical methods face limitations due to noise, sparsity, and resolution. In this work-in-progress, I explore a range of machine learning approaches — including Graph Neural Networks (GNNs), autoencoders, self-organizing maps (SOMs), contrastive learning, and clustering — for classifying cosmic environments from galaxy survey data. I compare the strengths and limitations of each method and assess their potential to complement or surpass traditional techniques in cosmic web analysis.