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.
Cite
@article{arxiv.1906.09382,
title = {A Halo Merger Tree Generation and Evaluation Framework},
author = {Sandra Robles and Jonathan S. Gómez and Adín Ramírez Rivera and Jenny A. González and Nelson D. Padilla and Diego Dujovne},
journal= {arXiv preprint arXiv:1906.09382},
year = {2019}
}
Comments
11 pages, 7 figures, 2 tables, 3 appendices. Presented at the ICML 2019 Workshop on Theoretical Physics for Deep Learning