Strong gravitational lensing combined with JWST’s spatio-spectral resolution enables resolved studies of star-forming regions in 2-4 galaxies, but identifying and characterizing such regions in lensed integral-field and multi-band data remains a manual, observer-dependent process. We present (Software for the Uniform Manifold Approximation of Clumps), an unsupervised learning pipeline that segments JWST imaging and spectroscopy at the “spaxel” level by combining -based manifold embedding with density clustering applied to spectral energy distributions/spectra. We demonstrate the pipeline on JWST/NIRSpec PRISM IFS observations of the lensed galaxy SGAS111020.0+645950.8 at , recovering six physically distinct stellar/nebular populations. The cluster median SEDs separate cleanly on the presence and strength of H+[OIII], H+[NII], slope, Balmer break strength, and the Balmer decrement, with bluer clusters tracing unobscured star-forming regions and progressively redder clusters tracing dusty star-forming regions.

生成讲解博客的中文版本

生成AI智能校验的中文版本