Invertible mapping between fields in CAMELS

Sambatra Andrianomena, Sultan Hassan, Francisco Villaescusa-Navarro

Keywords: generative adversarial networks, cosmology, neutral hydrogen, simulation, machine learning

TL;DR: This study aims at building bijective mapping between different observables in CAMELS dataset using CycleGAN.

Abstract: We build a bijective mapping between different fields from IllustrisTNG in CAMELS Project. In this work, 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 metrics such a 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 are 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 fake 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.