Merger Tree

Merger trees are hierarchical structures representing the assembly history of dark matter halos in cosmological simulations, crucial for understanding galaxy formation. Current research focuses on efficiently constructing these trees, leveraging machine learning techniques like Graph Neural Networks and Generative Adversarial Networks to overcome the computational limitations of traditional methods. These advancements enable faster and more accurate emulation of galaxy properties from merger tree data, improving our ability to model galaxy evolution across large cosmological volumes and ultimately leading to more realistic simulations. This improved efficiency allows for broader application in astrophysical studies and potentially facilitates the development of more sophisticated galaxy formation models.