AI 中文总结
研究利用正向建模框架，通过宇宙学模拟构建强度图，测量跨通道角功率谱等，展示多线强度映射可分离大尺度结构并探测尘埃衰减，还应用PCA光谱清理，量化线传递函数，凸显从相关观测中提取尘埃敏感可观测量的前景与挑战。
AI 中文摘要
线强度映射（LIM）通过测量未解析星系的总发射，提供星系演化的层析成像视图。在光学和近红外波段，线发射伴随着亮得多的连续谱发射，必须去除这些连续谱才能恢复线自功率谱和交叉功率谱。我们使用从宇宙学N体模拟中提取的约2平方度的光锥，通过基于物理的半解析模型（SAM）填充星系，开发了一个正向建模框架。我们构建了恒星连续谱和最强光学线（Hα、Hβ、[O III] λ5007和[O II] λ3727）的强度图，包括与星系属性相关的星云尘埃衰减。从这些立方体中，我们测量了跨通道角功率谱$C_\ell(\lambda_i,\lambda_j)$和归一化相关矩阵$r_{ij}$。在仅线的图中，相关矩阵显示了发射线之间的同红移脊，展示了多线强度映射（MLIM）如何分离大尺度结构并探测尘埃衰减。我们表明，尘埃对线交叉功率的抑制量取决于星系属性、波长和线对，因此单个总体幅度无法捕捉其效应。然而，在总图中，连续谱主导了原始相关性并隐藏了许多线脊结构。因此，我们应用基于主成分分析（PCA）的光谱清理，并使用模拟真值量化线传递函数。在我们的模拟图中，去除20个PCA模式在线恢复和连续谱抑制之间实现了最佳权衡。我们的结果展示了从类似SPHEREx的观测中提取对尘埃敏感的LIM可观测量的前景和挑战，并强调了在定量推断管道中对线清理及其传递函数进行建模的必要性。
英文摘要
Line intensity mapping (LIM) offers a tomographic view of galaxy evolution by measuring the aggregate emission from unresolved galaxies. In the optical and near-infrared, the line emission is accompanied by much brighter continuum emission that must be removed to recover line auto- and cross-power spectra. We develop a forward-modeling framework using a $\sim2$deg$^2$ lightcone drawn from a cosmological $N$-body simulation populated with galaxies using a physics-based semi-analytic model (SAM). We construct intensity maps for the stellar continuum and for the strongest optical lines, H$α$, H$β$, [O III] $\lambda5007$, and [O II] $\lambda3727$, including nebular dust attenuation tied to galaxy properties. From these cubes, we measure cross-channel angular power spectra, $C_\ell(λ_i,λ_j)$, and the normalized correlation matrix $r_{ij}$. In line-only maps, the correlation matrices show same-redshift ridges between emission lines, demonstrating how multi-line intensity mapping (MLIM) can isolate large-scale structure and probe dust attenuation. We show that dust suppresses the line cross-power by an amount that depends on galaxy properties, wavelength, and line pair, so a single overall amplitude cannot capture its effect. In total maps, however, the continuum dominates the raw correlations and hides much of the line-ridge structure. We therefore apply principal component analysis (PCA)-based spectral cleaning and quantify the line-transfer function using the simulation truth. Removing 20 PCA modes gives the best trade-off between line recovery and continuum suppression in our mock maps. Our results demonstrate the promise and challenges of extracting dust-sensitive LIM observables from SPHEREx-like observations, and highlight the need to model continuum cleaning and its transfer function in quantitative inference pipelines.
Comments28 pages, 18 figures, 2 tables
