Simulation-based Deep Sets model with neural posterior estimation halves scatter in cluster mass estimates from galaxy kinematics compared to the M-sigma relation.
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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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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.