We build a bijective mapping between different physical fields from hydrodynamic CAMELS simulations. We train a CycleGAN on three different setups: translating dark matter to neutral hydrogen (Mcdm-HI), mapping between dark matter and magnetic fields magnitude (Mcdm-B), and finally predicting magnetic fields magnitude from neutral hydrogen (HI-B). We assess the performance of the models using various summary statistics, such as the probability distribution function (PDF) of the pixel values and 2D power spectrum (P(k)). Results suggest that in all setups, the model is capable of predicting the target field from the source field and vice versa, and the predicted maps exhibit statistical properties which are consistent with those of the target maps. This is indicated by the fact that the mean and standard deviation of the PDF of maps from the test set is in good agreement with those of the generated maps. The mean and variance of P(k) of the real maps agree well with those of generated ones. The consistency tests on the model suggest that the source field can be recovered reasonably well by a forward mapping (source to target) followed by a backward mapping (target to source). This is demonstrated by the agreement between the statistical properties of the source images and those of the recovered ones.
Reference:
Invertible mapping between fields in CAMELS, Sambatra Andrianomena, Sultan Hassan, Francisco Villaescusa-Navarro, Accepted at the ICLR 2023 Workshop on Physics for Machine Learning (Camera-ready version), arXiv:2303.07473