BIND（基于深度学习的重子修补）：星系群和星系团的场级模拟器
BIND (Baryonic INpainting with Deep learning): A Field-level Emulator for Galaxy Groups and Clusters
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中文总结 AI 辅助
BIND利用条件流匹配模型，将仅暗物质暗晕场级映射至流体动力学对应物，在CAMELS模拟上训练，以百分比精度恢复质量场和轮廓，并开源发布。
中文摘要 AI 辅助
重子反馈是即将开展的弱引力透镜巡天中系统不确定性的主要来源，但当前用于模拟其效应的工具依赖于球对称近似和几乎完全基于两点统计量校准的密度轮廓。我们提出了BIND（基于深度学习的重子修补），一种条件流匹配模型，学习从仅含暗物质的暗晕到其流体动力学对应物的场级映射。BIND在CAMELS $50\\,h^{-1}\\,\mathrm{Mpc}$ SB35套件中1024对配对流体动力学和仅暗物质模拟的暗晕上进行训练，并在完整的35维$\Lambda$CDM和IllustrisTNG星系形成参数空间中，对暗物质、气体和恒星质量场进行红移采样。BIND以百分比精度恢复暗物质、气体和恒星质量，在所有半径处将方位角平均轮廓重现到$\lesssim10\\%$以内，并以高保真度匹配暗晕形状分布。学习到的参数依赖关系捕捉了生成场与亚网格参数之间的秩相关，且对单个参数变化的场级响应在符号和形态上均得到恢复。暗晕质量从未作为条件输入，但重子分数、恒星-暗晕质量关系、组分间标度关系及其残差的联合协方差均被重现。我们最终展示，将BIND逐个暗晕应用于一个具有$512^3$粒子的$(50\\,h^{-1}\\,\mathrm{Mpc})^3$ $N$体体积，BIND在单块GPU上数分钟内即可将投影物质功率谱抑制重现到由直接嵌入流体动力学暗晕所设定的精度上限。我们发布训练好的BIND模型和所有生成的暗晕作为开源工具。一篇配套论文将BIND扩展到热力学场和非高斯弱引力透镜统计量。
英文摘要
Baryonic feedback is a dominant source of systematic uncertainty for upcoming weak-lensing surveys, but current tools for modeling its effect rely on spherical approximations and density profiles calibrated almost entirely on two-point statistics. We introduce BIND (Baryonic INpainting with Deep learning), a conditional flow-matching model that learns a field-level mapping from dark-matter-only halos to their hydrodynamical counterparts. BIND is trained on halos from the 1024 paired hydrodynamical and dark-matter-only simulations of the CAMELS $50\,h^{-1}\,\mathrm{Mpc}$ SB35 suite and samples dark matter, gas, and stellar mass fields over redshift across the full 35-dimensional $Λ$CDM and IllustrisTNG galaxy formation parameter space. BIND recovers dark matter, gas, and stellar masses at the percent level, reproduces azimuthally averaged profiles to $\lesssim10\%$ at all radii, and matches halo shape distributions with high fidelity. The learned parameter dependence captures the rank correlations between the generated fields and the subgrid parameters, and the field-level response to individual parameter variations is recovered in both sign and morphology. Halo mass is never supplied as conditioning, yet the baryon fraction, stellar-to-halo mass relation, inter-component scaling relations, and the joint covariance of their residuals are all reproduced. We finally show that, applied halo-by-halo to a $(50\,h^{-1}\,\mathrm{Mpc})^3$ $N$-body volume with $512^3$ particles, BIND reproduces the projected matter power spectrum suppression to the accuracy ceiling set by pasting in the hydrodynamical halos themselves, in minutes on one GPU. We release the trained BIND models and all generated halos as open-source tools. A companion paper extends BIND to thermodynamic fields and non-Gaussian weak-lensing statistics.
发表机构
- Columbia University（哥伦比亚大学）
- Flatiron Institute（平顿研究所）
- Institute of Science and Technology Austria（奥地利科学技术研究所）
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