Reading Between the Lines: Forward Modeling Dust, Continuum, and Spectral Cleaning for Multi-Line Intensity Mapping
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$\alpha$, H$\beta$, [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, $C_\ell(\lambda_i,\lambda_j)$, 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.