发表机构
Cornell University（康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种神经网络模拟器，直接映射宇宙学参数、晕质量与红移至累积晕数量，避免分箱与PCA信息损失，并在三个模拟套件上验证，88%预测偏差低于散粒噪声，提供内置置信度，可用于宇宙学推断。
AI 中文摘要
星系团丰度随质量的变化——即暗物质晕质量函数（HMF）——强烈依赖于宇宙学参数。大规模模拟计算成本高昂，因此需要能够对模拟输出进行插值的模拟器。我们提出了一种神经网络模拟器，它将宇宙学与天体物理参数、暗物质晕质量以及红移直接映射到高于输入质量的暗物质晕的累积数量，从而避免了先前模拟器中因分箱以及基于函数或PCA的数据缩减所固有的信息损失。每个模拟器的预测都附带一个自估计误差，以泊松散粒噪声为单位表示，网络在预测暗物质晕数量的同时学习预测该误差——从而为每个输出提供内置的置信度指标。我们通过留一法测试对三个模拟套件——N体模拟套件MassiveNus以及流体动力学模拟套件CAMELS IllustrisTNG和SIMBA——进行了验证，这些套件涵盖了不同的盒子尺寸、红移、亚网格物理以及软件。在所有留出预测（总计$1.2\ imes 10^6$）中，88%的模型偏差等于或低于散粒噪声，99.9%的偏差在$3\ imes$散粒噪声以内。唯一显著的高误差尾部出现在MassiveNus中，其中预测的模型偏差标记出76%的预测超过$3\ imes$散粒噪声；其余高误差预测的数量与仅由散粒噪声预期的数量相当。该模拟器公开可用，并且可以在其他模拟套件上进行训练，使其成为一个灵活的HMF工具，可用于宇宙学和天体物理推断。
英文摘要
The abundance of galaxy clusters as a function of mass --- the halo mass function (HMF) --- depends strongly on cosmological parameters. Large-scale simulations are computationally expensive, motivating emulators that interpolate simulation outputs. We present a neural network emulator that maps cosmological and astrophysical parameters, halo mass, and redshift directly to a cumulative count of halos above the input mass, avoiding the information loss inherent to binning and to the functional or PCA-based data reduction used in prior emulators. Each emulator prediction is accompanied by a self-estimated error, expressed in units of Poisson shot noise, that the network learns to predict alongside the halo count itself --- giving every output a built-in confidence metric. We validate the emulator with leave-one-out tests on three simulation suites --- the N-body MassiveNus suite and the hydrodynamical CAMELS IllustrisTNG and SIMBA suites --- spanning a range of box sizes, redshifts, sub-grid physics, and softwares. Of all the held-out predictions ($1.2\times 10^6$ in total), 88\% have model bias at or below shot noise and 99.9\% are within $3\times$ shot noise. The only significant high-error tail is in MassiveNus, where the predicted model bias flags 76\% of predictions exceeding $3\times$ shot noise; the remaining number of high error predictions is comparable to the number expected from shot noise alone. This emulator is publicly available and can be trained on other simulation suites making it a flexible HMF tool that can be used for cosmological and astrophysical inference.
