一种银河系中心分子区三维尘埃分布测绘的新方法
A Novel Approach to 3D Dust Mapping of the Central Molecular Zone
- Università dell’Insubria（因苏布里亚大学）
- University College London（伦敦大学学院）
- University of Florida（佛罗里达大学）
- University of Connecticut（康涅狄格大学）
- Instituto de Astrofísica de Andalucía (CSIC)（安达卢西亚天体物理学研究所（西班牙国家科学研究委员会））
- University of Cambridge（剑桥大学）
- Universität Heidelberg（海德堡大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
提出一种利用恒星自行和消光数据、基于核恒星盘模型推断银河系中心分子区三维尘埃分布的非参数新方法，经模拟验证有效，可独立于现有方法提供全面尘埃图。
中文摘要 AI 辅助
银河系中心分子区（CMZ）中尘埃和气体的三维分布，是理解气体向银河系中心（GC）流入、这一极端环境中恒星形成过程，以及源自人马座A*（Sgr A*）的高能宇宙射线传播的关键。然而，尽管近期的工作已在贝叶斯框架下结合多种数据集来估计CMZ中单个分子云的近/远位置，不同方法之间仍存在冲突，我们仍缺乏一个全面的、与模型无关的CMZ中所有气体和尘埃的分布图，而这对于解决关键科学问题至关重要。在此，我们开发了一种新方法来推断CMZ的三维尘埃分布。该方法的核心思想是利用恒星自行，通过核恒星盘（NSD）——与CMZ共空间——的恒星位置和速度分布模型，获取未知恒星距离的概率信息。以恒星自行和消光（后者作为尘埃柱密度的替代量）为输入，该方法输出三维尘埃分布。它是非参数化的，对尘埃分布不作任何先验假设，并且与所有现有方法有根本性的区别，且在很大程度独立于它们。我们通过在一系列解析生成的和取自流体动力学模拟的模拟尘埃分布上测试该方法，证明其能够稳健且有效地重建模拟的CMZ三维结构。最后，我们讨论了将该方法应用于真实数据的前景。
英文摘要
The 3D distribution of dust and gas in the Milky Way's Central Molecular Zone (CMZ) is key to understanding gas inflows toward the Galactic Centre (GC), the process of star formation in this extreme environment, and the propagation of energetic cosmic rays originating from Sgr A*. However, while recent efforts have combined datasets in a Bayesian framework to estimate the near/far positions of individual molecular clouds in the CMZ, conflicts between different methodologies still remain and we are still lacking a comprehensive, model-independent map of all of the gas and dust in the CMZ, which is critical to address key science questions. Here we develop a new methodology to infer the 3D dust distribution of the CMZ. The key idea of the method is to use \emph{stellar} proper motions to get probabilistic information about the unknown stellar distances through a model of the distribution of star positions and velocities of the nuclear stellar disc (NSD), co-spatial to the CMZ. Taking \emph{stellar} proper motions and extinctions as input, the latter adopted as a proxy of the dust column density, the method returns the 3D dust distribution. It is non parametric, makes no a-priori assumption on the dust distribution, and is fundamentally distinct and largely independent of all existing methods. We show that the method can robustly and effectively reconstruct the mock 3D CMZ structure by testing it on a range of mock dust distributions, both analytically generated and taken from hydrodynamical simulations. Finally, we discuss the prospects for applying the method to real data.
