AI 中文总结
Diffhalos是一种暗物质晕的宇宙光锥生成模型，通过抽取晕质量函数样本、利用归一化流生成质量聚集历史等方法，能准确生成相关样本，还可计算质量函数梯度，用于星系宇宙学理论预测和生成模拟星系目录。
AI 中文摘要
我们提出了一种暗物质晕的宇宙光锥生成模型Diffhalos。在该模型中，我们使用基于JAX的晕模型Halox在光锥中抽取晕质量函数的蒙特卡罗样本，并通过从条件子晕质量函数模型中抽样来生成子晕样本。我们利用在宇宙学N体模拟中的合并树上训练的归一化流来生成质量聚集历史（MAHs）。我们表明，Diffhalos可以生成晕、子晕及其MAHs的样本，其统计分布能准确近似模拟光锥中的总体。作为一个示例应用，我们用Diffhalos计算晕和子晕质量函数相对于宇宙学参数的梯度。最后，我们讨论了正在进行的工作，即使用Diffhalos与星系 - 晕连接模型一起对星系的宇宙学总体进行理论预测，并生成模拟星系目录。
英文摘要
We present a generative model of cosmological lightcones of dark matter halos, Diffhalos. In our model, we draw Monte Carlo samples of the halo mass function in a lightcone with a JAX-based implementation of the halo model, Halox, and we generate samples of subhalos by drawing from a model for the conditional subhalo mass function. We generate mass assembly histories (MAHs) using a normalizing flow trained on merger trees in cosmological N-body simulations. We show that Diffhalos can generate samples of halos, subhalos, and their MAHs with a statistical distribution that accurately approximates populations in simulated lightcones. As an example application, we use Diffhalos to calculate gradients of the halo and subhalo mass functions with respect to cosmological parameters. We conclude with a discussion of ongoing work using Diffhalos together with models of the galaxy--halo connection to make theoretical predictions for cosmological populations of galaxies, and to generate mock galaxy catalogs.
Comments10 pages, 9 figures, source code publicly available at https://github.com/ArgonneCPAC/diffhalos
