University of Nottingham

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.

Panvascular intervention research is starting to write traceability into an agent's output contract: ScienceBuddy-PV fixes a shared convolutional model on the public ARCADE coronary angiography benchmark and evaluates only an evidence gate and action audit, where the gate accepts 76.7% of stenosis-frame candidates against 100% for a score-only comparator. On the locked test set, stenosis Dice is 0.231 and AUROC is 0.957 at a pixel prevalence of 0.00975, while reference lesion candidates are accepted 44.3% and rejected 55.7% of the time. The result is an image-level technical validation; recursive harness optimization and model learning remain protocols rather than executed experiments.

Panvascular intervention research is starting to write traceability into an agent's output contract: ScienceBuddy-PV fixes a shared convolutional model on the public ARCADE coronary angiography benchmark and evaluates only an evidence gate and action audit, where the gate accepts 76.7% of stenosis-frame candidates against 100% for a score-only comparator. On the locked test set, stenosis Dice is 0.231 and AUROC is 0.957 at a pixel prevalence of 0.00975, while reference lesion candidates are accepted 44.3% and rejected 55.7% of the time. The result is an image-level technical validation; recursive harness optimization and model learning remain protocols rather than executed experiments.