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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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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A vision transformer classifier trained on simulated and real Euclid data recovers all known strong lenses in test sets and finds 8 Grade A plus 26 Grade B new candidates in the Q1 data.
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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Euclid Quick Data Release (Q1). AstroVink: A vision transformer approach to find strong gravitational lens systems
A vision transformer classifier trained on simulated and real Euclid data recovers all known strong lenses in test sets and finds 8 Grade A plus 26 Grade B new candidates in the Q1 data.