A deep learning approach to halo merger tree construction. (arXiv:2205.15988v1 [astro-ph.GA])

A key ingredient for semi-analytic models (SAMs) of galaxy formation is the
mass assembly history of haloes, encoded in a tree structure. The most commonly
used method to construct halo merger histories is based on the outcomes of
high-resolution, computationally intensive N-body simulations. We show that
machine learning (ML) techniques, in particular Generative Adversarial Networks
(GANs), are a promising new tool to tackle this problem with a modest
computational cost and retaining the best features of merger trees from
simulations. …

arxiv astro construction deep learning learning merger tree