A Halo Merger Tree Generation and Evaluation Framework
This provides a computational tool for astrophysicists to efficiently generate realistic halo merger trees, though it is incremental as it builds on existing GAN methods applied to a specific domain.
The authors tackled the problem of generating realistic halo merger trees for semi-analytic galaxy formation models by proposing a Generative Adversarial Network framework that learns from large simulations like EAGLE, showing quality results with modest computational cost.
Semi-analytic models are best suited to compare galaxy formation and evolution theories with observations. These models rely heavily on halo merger trees, and their realistic features (i.e., no drastic changes on halo mass or jumps on physical locations). Our aim is to provide a new framework for halo merger tree generation that takes advantage of the results of large volume simulations, with a modest computational cost. We treat halo merger tree construction as a matrix generation problem, and propose a Generative Adversarial Network that learns to generate realistic halo merger trees. We evaluate our proposal on merger trees from the EAGLE simulation suite, and show the quality of the generated trees.