BIND：光锥绑定——一套天体物理射线追踪弱引力透镜与SZ（苏尼亚耶夫-泽尔多维奇）效应图
BINDing the lightcone: A suite of astrophysical ray-traced weak lensing and SZ maps
- 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）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对重子反馈建模不确定性，提出基于深度学习的BIND模型生成射线追踪弱引力透镜和SZ效应图，验证精度达LSST-Y10水平，并揭示反馈响应可压缩为七个晕属性。
AI中文摘要：
近期对星系群和星系团气体的多波段观测表明，重子反馈效应强于我们经过最佳校准的流体动力学模拟所产生的结果，而对此反馈的建模仍是第四代弱引力透镜（WL）分析中一个主要的不确定性来源。我们提出了一套使用BIND（基于深度学习的重子修补）生成的射线追踪图，BIND是一种条件流匹配模型，可将重子质量和气体热力学性质绘制到纯暗物质模拟的暗物质晕上，该模型在配套论文中开发。将其应用于IllustrisTNG300-Dark并经过射线追踪，我们在五个源红移处生成了会聚图、光学深度图和康普顿-y图，每个红移均有1000个伪独立实现。我们构建了跨越三十维IllustrisTNG星系形成先验的256节点Sobol序列光锥，包括单个参数变化和基准模型。在验证中，这些图与从IllustrisTNG300暗物质晕构建的图在多种WL统计量上匹配，精度达到类似LSST-Y10的水平。在先验范围内，小尺度上对反馈的响应超过第四代统计精度一个数量级以上，且不同统计量对不同模型部分响应不同：星系风控制WL功率谱及气体自谱和交叉谱，而恒星初始质量函数斜率和活动星系核（AGN）参数塑造形态统计量（概率密度函数、峰值、谷值和闵可夫斯基泛函）。最后，我们发现统计量的响应可压缩为七个暗物质晕属性，这些属性可线性预测一系列WL和SZ统计量。我们公开发布这些图、统计量和模型表格。
英文摘要：
Recent multiwavelength observations of galaxy group and cluster gas suggest stronger baryonic feedback than our best-calibrated hydrodynamical simulations produce, while modeling this feedback remains a primary source of uncertainty in Stage-IV weak-lensing (WL) analyses. We present a suite of ray-traced maps generated with BIND (Baryonic INpainting with Deep learning), a conditional flow-matching model that paints baryonic mass and gas thermodynamics onto the halos of dark-matter-only simulations, and which was developed in a companion paper. Applied to IllustrisTNG300-Dark and ray-traced, we generate convergence, optical depth, and Compton-$y$ maps at five source redshifts, each with $1000$ pseudo-independent realizations. We build lightcones across a 256-node Sobol sequence spanning the thirty-dimensional IllustrisTNG galaxy formation prior, with individual parameter variations and at the fiducial model. In validation, the maps match those built from the IllustrisTNG300 halos to within LSST-Y10-like precision for a range of WL statistics. Across the prior, the response to feedback exceeds Stage-IV statistical precision by more than an order of magnitude on small scales, and different statistics respond to different model sectors: galactic winds control the WL power spectrum and gas auto- and cross-spectra, while the stellar initial mass function slope and AGN parameters shape the morphological statistics (PDF, peaks, minima, and Minkowski functionals). Finally, we find that the response of statistics can be compressed into seven halo properties which linearly predict a range of WL and SZ statistics. We publicly release the maps, statistics, and model tables.
