发表机构
Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于Transformer的多示踪剂融合超分辨框架，从数据推断小尺度极化尘埃结构，生成20角分分辨率的Q/U图，优于PySM模型，为CMB前景建模提供新途径。
AI 中文摘要
高角分辨率的银河系极化尘埃发射的真实模型对于下一代宇宙微波背景（CMB）B模式实验中的成分分离至关重要。然而，现有的尘埃极化模板仅被限制在约1度的尺度，并且开发能够生成真实的小尺度复杂性且与已测量的大尺度结构连贯连接的模型仍然具有挑战性。在本工作中，我们开发了一个数据驱动的超分辨框架，直接从多个高分辨率观测的示踪剂预测小尺度尘埃极化结构。我们将普朗克尘埃光学深度τ353、基于中性氢（H I）的斯托克斯模板以及粗分辨率的普朗克GNILC Q和U图结合在一个基于Transformer的模型中，该模型被训练用于从自身的4倍波束平滑版本恢复原生分辨率的GNILC极化，从而使网络解决一个由多个示踪剂结构信息引导的去卷积问题，而不是生成结构以匹配目标统计量。将该模型应用于均匀分辨率的GNILC图，我们在高纬度（|b|>30度）天空上以20角分分辨率生成Q和U预测。使用标量和张量Minkowski泛函以及散射变换统计量，我们发现我们的图展现出比最新的PySM尘埃模型更连贯、各向异性和丝状的小尺度结构。我们的结果确立了数据驱动的学习去卷积作为前景模型的一种有前景的途径，其小尺度结构是从数据推断而非预设的。
英文摘要
Realistic models of polarized Galactic dust emission at high angular resolution are essential for component separation in next-generation cosmic microwave background (CMB) $B$-mode experiments. However, existing dust polarization templates are constrained only to ~1 degrees, and it remains challenging to develop models that can generate realistic small-scale complexity that is also coherently connected to the well-measured large-scale structures. In this work, we develop a data-driven super-resolution framework that predicts small-scale dust polarization structure directly from multiple tracers observed at high resolution. We combine the Planck dust optical depth $τ_{353}$, \ion{H}{1}-based Stokes templates, and coarse Planck GNILC $Q$ and $U$ maps in a transformer-based model trained to recover native-resolution GNILC polarization from a $4\times$ beam-smoothed version of itself, so that the network solves a deconvolution problem informed by the structure of multiple tracers rather than generating structure to match a target statistic. Applying the model to the uniform-resolution GNILC maps, we produce $Q$ and $U$ predictions at 20' over the high-latitude (|b|>30 degrees) sky. Using scalar and tensorial Minkowski functionals and scattering transform statistics, we find that our maps exhibit more coherent, anisotropic, and filamentary small-scale structure than the latest PySM dust model. Our results establish data-driven learned deconvolution as a promising route to foreground models whose small scales are inferred from data rather than prescribed.
