When I first started working with integral field spectroscopic (IFU) data, I was struck by how much complexity was being averaged out or masked by traditional processing techniques. Most segmentation methods in astronomy—especially those designed for IFU data cubes—rely either on predefined morphological components or on signal-to-noise heuristics. Among the most common is Voronoi binning, which prioritizes the signal-to-noise ratio at the expense of preserving the underlying spectral variation.
AI-powered algorithm Capivara unmasks hidden structures in galaxies by analyzing their spectral fingerprints
