Making the Invisible Visible: Converting Multi-band Astronomical Data into RGB Images

Modern astronomy relies on multi-wavelength observations across the electromagnetic spectrum, yet most of these data lie outside the optical regime. Since different physical processes emit radiation at characteristic energies, no single band can capture the full picture of a complex astronomical environment. We introduce a Python pipeline for converting multi-wavelength astronomical observations into scientifically representative pseudo-color composite images. The workflow incorporates FITS ingestion, statistical profiling, chromatic response modeling, and energy-ordered RGB projection. Each step addresses a specific challenge: reading heterogeneous data formats, characterising dynamic range, modelling human colour response, and projecting multiple bands into a perceptually meaningful image. It is found that the arcsinh stretch achieves optimal contrast across high dynamic ranges, compared with four intensity scaling laws: linear, logarithmic, arcsinh, and power-law, on Chandra observations of Cassiopeia A and the Pinwheel Galaxy (M101). This is because arcsinh behaves linearly at low intensities and logarithmically at high intensities, preserving both faint and bright structures within a single display. Additionally, percentile clipping and three-channel color assignment allow spatially overlapping emission structures to be distinguished visually. This lightweight framework provides a transparent tool for generating scientifically meaningful astronomical illustrations.