摘要
Merger trees track the hierarchical assembly of dark matter haloes across cosmic time and serve as essential inputs for semi-analytic models (SAMs) of galaxy formation. However, conventional methods for constructing merger trees rely on ad-hoc assumptions and are unable to incorporate environmental information. Nguyen et al. introduced florah, a generative model based on recurrent neural networks and normalizing flows, for modelling main progenitor branches of merger trees. In this work, we extend this model, now referred to as florah-tree, to generate complete merger trees by representing them as graph structures that capture the full branching hierarchy. We trained florah-tree on merger trees extracted from the Very Small MultiDark Planck cosmological N-body simulation. To validate our approach, we compared the generated merger trees with both the original simulation data and with semi-analytic trees produced using the Extended Press-Schechter (EPS) formalism. We show that florah-tree accurately reproduces key merger rate statistics across a wide range of mass and redshift, outperforming the conventional EPS-based approach. We demonstrate its utility by applying the Santa Cruz SAM to generated trees and showing that the resulting galaxy-halo scaling relations, such as the stellar-to-halo-mass relation and supermassive black hole mass-halo mass relation, closely match those from applying the SAM to trees extracted directly from the simulation. florah-tree provides a computationally efficient method for generating merger trees that maintain the statistical fidelity of N-body simulations.