Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

arXiv:2311.01588v3 Announce Type: replace-cross
Abstract: Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance …

abstract arxiv astro-ph.co cs.ai cs.lg data data sets deep learning differences domain graph graph neural networks however implementation information multiple networks neural networks numerical parameters physics power simulation spectrum statistics summary type