Galactification: painting galaxies onto dark matter only simulations using a transformer-based model
Shivam Pandey, Christopher C. Lovell, Chirag Modi, Benjamin D. Wandelt
将星系的形成和演化与大规模结构联系起来对于解释宇宙学观测至关重要。 虽然流体力学模拟准确地模拟了星系的相关性,但它们在计算上是令人望而却步的,无法超过与现代调查相匹配的体积。 我们通过开发一个框架来解决这个问题,以快速生成以廉价的暗物质模拟为条件的模拟星系目录。 我们提出了一个多模态,基于变压器的模型,该模型将3D暗物质密度和速度场作为输入,并输出具有物理特性的相应点星系云。 我们证明,我们经过训练的模型忠实地再现了各种星系汇总统计数据,并随着底层宇宙学和天体物理参数的变化正确捕获它们的变异,使其成为第一个加速向前模型,以捕获所有相关星系属性,它们的全部空间分布及其在水力模拟中的条件依赖性。
Connecting the formation and evolution of galaxies to the large-scale structure is crucial for interpreting cosmological observations. While hydrodynamical simulations accurately model the correlated properties of galaxies, they are computationally prohibitive to run over volumes that match modern surveys. We address this by developing a framework to rapidly generate mock galaxy catalogs conditioned on inexpensive dark-matter-only simulations. We present a multi-modal, transformer-based model th...