A CNN-GNN fusion model estimates triaxial cluster geometry from 2D X-ray, tSZ, and galaxy data in MillenniumTNG simulations, improving over spherical assumptions by 30% with R²=0.85 on major axis length and 71% accuracy on line-of-sight prolate orientations.
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Simulations match observed outflow masses within 0.5 dex but underpredict velocities by an order of magnitude and show face-on galaxies 15-40% more likely to exhibit detectable outflows than edge-on systems.
IllustrisTNG simulations link filament density to galaxy morphology trends across redshifts and predict that Roman's planned HLWAS survey needs greater depth to accurately map the z=1 cosmic web.
citing papers explorer
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Estimating the triaxiality of massive clusters from 2D observables in MillenniumTNG with machine learning
A CNN-GNN fusion model estimates triaxial cluster geometry from 2D X-ray, tSZ, and galaxy data in MillenniumTNG simulations, improving over spherical assumptions by 30% with R²=0.85 on major axis length and 71% accuracy on line-of-sight prolate orientations.
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High-Redshift Galactic Outflows: Orientation Effects, Kinematics, and Metallicity in TNG50 and SERRA
Simulations match observed outflow masses within 0.5 dex but underpredict velocities by an order of magnitude and show face-on galaxies 15-40% more likely to exhibit detectable outflows than edge-on systems.
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Impact of Cosmic Filaments on Galaxy Morphological Evolution and Predictions of Early Cosmic Web Structure for Roman
IllustrisTNG simulations link filament density to galaxy morphology trends across redshifts and predict that Roman's planned HLWAS survey needs greater depth to accurately map the z=1 cosmic web.