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. We train our GAN model with a limited sample of merger trees from the EAGLE simulation suite, constructed using two halo finders-tree builder algorithms: SUBFIND-D-TREES and ROCKSTAR-ConsistentTrees. Our GAN model successfully learns to generate well-constructed merger tree structures with high temporal resolution, and to reproduce the statistical features of the sample of merger trees used for training, when considering up to three variables in the training process. These inputs, whose representations are also learned by our GAN model, are mass of the halo progenitors and the final descendant, progenitor type (main halo or satellite) and distance of a progenitor to that in the main branch. The inclusion of the latter two inputs greatly improves the final learned representation of the halo mass growth history, especially for SUBFIND-like ML trees. When comparing equally sized samples of ML merger trees with those of the EAGLE simulation, we find better agreement for SUBFIND-like ML trees. Finally, our GAN-based framework can be utilised to construct merger histories of low and intermediate mass haloes, the most abundant in cosmological simulations.
翻译:银河系形成半分析模型(SAMS)的一个关键要素是成形成形的光圈的大规模组装史。建造光圈合并史最常用的方法是基于高分辨率、计算密集的N体模拟的结果。我们显示,机器学习技术,特别是基因反转网络(GANs)是解决该问题的有希望的新工具,其计算成本较低,并保留了模拟中合并树的最佳特征。我们从EAGLE模拟套件中用有限的合并树样本来培训我们的GAN模型,该模型使用两种中间的光圈查找树类合并历史结构:AFID-D-TREES和ROCKSTAR- ConsentTre 模拟。我们GAN模型成功地学会了以高时间分辨率制造精密的复合树结构,并且复制了用于培训的合并树类最终样本的统计特征,在培训过程中发现了三个变量。这些投入也是由我们GAN类的模拟模型所了解的合并树类的合并树样样样本,特别是用于将GRO-ROML的原始树类最终结构、GLMLMLM-romainal的原始结构结构的升级结构,最终可以改进到Gral-rocial-roal-roal、Groal-roal-roal-roal、Groal-roal-roal-roal-roal-roal-roal-al-al-deal-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-Ial-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-al-