2026ApJ...997..339H

Analyses of integral field unit data are typically performed on a per-spaxel basis, with each spectrum modelled independently. For low signal-to-noise ratio (S/N) features such as weak emission lines, estimating properties is difficult and imprecise. Arbitrary binning schemes boost S/N at the cost of resolution, and risk introducing biases. We present a general forward-modeling approach that assumes spectra close on the sky are more similar than distant ones, and so can be modelled jointly. These “spectrospatial” models exploit spatial correlation to provide robust inferences, while simultaneously providing continuous predictions of line properties like strength and kinematics across the sky. Instrumental and calibration systematics are straightforward to include and infer. The model provides a natural trade-off between spatial resolution and S/N in a data-driven way. We apply this to Sloan Digital Sky Survey V Local Volume Mapper (LVM) data of the Rosette Nebula, producing continuous maps of fluxes and kinematics for Balmer, nebular, and auroral lines, as well as weak C II and N II recombination lines, demonstrating the approach across 3 orders of magnitude in S/N, including in the very low-S/N regime. The method recovers identical morphologies across different lines tracing similar ionisation volumes, at varying resolutions set by the S/N. We additionally provide a general framework for building and fitting such models in JAX, suitable for many applications. The implementation is fast and memory efficient, scales to large data volumes as in LVM, and can be deployed on hardware accelerators.