J-comb: An image fusion algorithm to combine observations covering different spatial frequency ranges

Ground-based, high-resolution bolometric (sub)millimeter continuum mapping observations on spatially extended target sources are often subject to significant missing fluxes. This hampers accurate quantitative analyses. Missing flux can be recovered by fusing images with that preserve structures. However, the commonly adopted image fusion approaches do not maintain simplicity of beam response function and try elaborate details yielded functions. These make comparison at multiple wavelengths straightforward. We present a new algorithm, J-comb, which combines high low-resolution linearly. By applying taper low-pass filtered combining it high-pass using proper weights, functions our combined guaranteed have near-Gaussian shapes. makes easy convolve share same Moreover, we introduce strategy tackle specific problem imaging 850 µm from present-date ground-based instrument taken Planck satellite overlap in Fourier domain. benchmarked method against two other widely-used combination algorithms, CASA-feather MIRIAD-immerge, mock star-forming molecular clouds. demonstrate performance J-comb algorithm is superior those algorithms. applied real observational data Orion A region. successfully produced dust temperature column density maps ?10? angular resolution, unveiling much greater than previous results. python code release implementation available https://github.com/SihanJiao/J-comb .