ERGO-ML: comparing IllustrisTNG and HSC galaxy images via contrastive learning
Modern cosmological hydrodynamical galaxy simulations provide tens of thousands of reasonably realistic synthetic galaxies across cosmic time. However, quantitatively assessing the level of realism of simulated universes in comparison to the real one is difficult. In this paper of the Extracting Reality from Galaxy Observables with Machine Learning
Eisert, Lukas et al.
Fecha de publicación:
3
2024