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arXiv 2602.04528astro-ph.COastro-ph.GAastro-ph.IM
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利用散射变换分离普朗克观测中的偏振尘埃发射

Separation of polarized dust emission in Planck observations with Scattering Transforms

Laboratoire de Physique de l’École Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris Cité(巴黎高等师范学院物理实验室,ENS,PSL大学,CNRS,索邦大学,巴黎西岱大学);Lawrence Berkeley National Laboratory(劳伦斯伯克利国家实验室);Department of Astronomy and Astrophysics, University of Chicago(芝加哥大学天文学与天体物理学系);CNRS-UCB International Research Laboratory, Centre Pierre Binétruy, IRL 2007, CPB-IN2P3(CNRS-UCB国际研究实验室,皮埃尔·比内屈中心,IRL 2007,CPB-IN2P3)

Alexandros Tsouros, Elisa Russier, Erwan Allys, Constant Auclair, François Boulanger, Jacques Delabrouille

AI总结:

本文提出利用散射变换从普朗克数据中分离偏振尘埃发射,通过非高斯结构和数据驱动分析提高尘埃偏振地图的精度和一致性。

摘要:

偏振尘埃发射是宇宙微波背景偏振(CMB)的主要天体物理前景污染源,必须准确测量以寻找微波背景中微弱的原初偏振B模。到目前为止,从普朗克空间任务数据获得的地图在高银河纬度区域(最相关的CMB观测区域)中主要受噪声主导。本工作的目标是通过利用偏振与总强度之间的依赖关系,以及尘埃发射的非高斯纤维状结构,从普朗克观测中获得更好的尘埃偏振地图。为此,我们使用散射变换,它能提供复杂非高斯纹理的稳定且可解释的表示,允许数据驱动的分析方法,无需对尘埃显式先验假设。分析在笛卡尔天空补丁上局部进行,其中线性偏振参数在局部参考系中重新定义,被建模为感兴趣的信号和干扰项的总和。通过多次随机干扰项的实现,我们通过最小化一个复合目标函数来恢复偏振尘埃地图,该函数在散射空间中施加了多个统计约束。所提出的算法重建了偏振尘埃发射的地图,其统计特性与普朗克数据一次随机干扰项添加后预期的统计特性一致。这在使用高信噪比天空区域作为测试用例的验证测试中得到确认。与现有尘埃偏振地图和模型的比较表明,我们的方法更好地恢复了小尺度偏振尘埃发射,且我们重建的功率和交叉谱与尘埃偏振地图的谱非常吻合。还产生了一组确定性地再现尘埃偏振发射特征的第二组地图。

英文摘要:

Polarized dust emission is a major astrophysical foreground contaminant of the cosmic microwave background polarization (CMB), which must be accurately measured to look for the faint primordial polarization B-modes of inflationary origin. The available maps to date, obtained from Planck space mission data, are noise-dominated in the high Galactic latitude regions that are most relevant for CMB observations. The goal of this work is to obtain better dust polarization maps from Planck observations, by exploiting both the dependence between polarization and total intensity, as well as the non-Gaussian filamentary structure of the dust emission. To this end, we use scattering transforms, which provide a stable and interpretable representation of complex non-Gaussian textures, allowing for a data-driven analysis approach requiring no explicit priors on dust. The analysis is performed locally on Cartesian patches of sky, where Stokes linear polarization parameters, redefined in a local reference frame, are modeled as the sum of a signal of interest and a nuisance term. Using multiple realizations of the random nuisance term, we recover the polarized dust maps by minimizing a composite objective function that enforces multiple statistical constraints in scattering space. The proposed algorithm reconstructs maps of polarized dust emission whose statistics are consistent with those expected from the Planck data once random nuisance realizations are added. This is confirmed in a validation test using a high signal-to-noise sky region as a test case. Comparisons with existing dust polarization maps and models show that our approach better recovers small-scale polarized dust emission, and that our reconstructed power and cross-spectra closely match those of the dust polarization maps. A second set of maps that deterministically reproduce the features of the dust polarized emission is also produced.

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