# Lyα Emission Cubes

A team including Caltech researchers trained a two-dimensional convolutional neural network, CDE, to invert low signal-to-noise Lyman-alpha integral-field cubes into line-of-sight integrated gas column-density maps. Training data come from IllustrisTNG100, post-processed with simplified Lyman-alpha radiative transfer and an approximate treatment of KCWI noise. On the simulated test set, CDE reaches a median linear RMSE of 1.48×10²⁰ cm^-2 and a median image SSIM of 0.635, both better than a pixel-wise power-law empirical baseline; applied to KCWI observations of the CC2.2 and Hyperion protoclusters, the predicted maps show filamentary structures extending about 40 arcseconds or more, though the results remain conditional on the adopted simulation and radiative-transfer assumptions.

A team including Caltech researchers trained a two-dimensional convolutional neural network, CDE, to invert low signal-to-noise Lyman-alpha integral-field cubes into line-of-sight integrated gas column-density maps. Training data come from IllustrisTNG100, post-processed with simplified Lyman-alpha radiative transfer and an approximate treatment of KCWI noise. On the simulated test set, CDE reaches a median linear RMSE of 1.48×10²⁰ cm^-2 and a median image SSIM of 0.635, both better than a pixel-wise power-law empirical baseline; applied to KCWI observations of the CC2.2 and Hyperion protoclusters, the predicted maps show filamentary structures extending about 40 arcseconds or more, though the results remain conditional on the adopted simulation and radiative-transfer assumptions.