Kavli Affiliate: Brian Nord
| First 5 Authors: Andrea Roncoli, Aleksandra Ćiprijanović, Maggie Voetberg, Francisco Villaescusa-Navarro, Brian Nord
| Summary:
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 when applied to
observational data. Training on data from two different suites of the CAMELS
hydrodynamic cosmological simulations, we examine the generalization
capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing
GNNs, we capitalize on their capacity to capture structured scale-free
cosmological information from galaxy distributions. Moreover, by including
unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable
our models to extract domain-invariant features. We demonstrate that DA-GNN
achieves higher accuracy and robustness on cross-dataset tasks (up to $28%$
better relative error and up to almost an order of magnitude better $chi^2$).
Using data visualizations, we show the effects of domain adaptation on proper
latent space data alignment. This shows that DA-GNNs are a promising method for
extracting domain-independent cosmological information, a vital step toward
robust deep learning for real cosmic survey data.
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