JESSNet: Joint dEconvolution and Sparse Separation Network for HI 21 cm foreground cleaning

Foreground cleaning is a major challenge for H, i 21 cm intensity mapping, particularly in the presence of a chromatic instrumental beam that couples angular foreground structure into the frequency direction and under incomplete sky coverage. We introduce JESSNet, a beam-aware, mask-compatible foreground-cleaning framework for single-dish intensity mapping. Building on SDecGMCA, JESSNet introduces three main extensions: a multiscale angular decomposition with scale-dependent mixing matrices and channel selection, a learned spherical-wavelet ''learnlet'' sparse regularization operator, and a mask-constrained reconstruction applicable to Galactic masks and survey footprints. We test JESSNet on simulated SKA-MID-like observations over 900-1300, MHz (z 0.09-0.58), including H, i emission, Galactic and extragalactic foregrounds, thermal noise, and an oscillating MeerKAT-inspired chromatic beam. The multiscale reconstruction accurately recovers the angular and frequency power spectra of the input H, i signal, improves the recovery at low and intermediate angular scales relative to a single-scale implementation, and reduces residual beam-induced spectral features. JESSNet also remains effective for a synthetic survey footprint, preserving the H, i power spectra under incomplete sky coverage. The code and analysis pipeline are publicly released to facilitate reproducibility and future applications to simulated and observational data.