Cosmological multifield emulator

We present the application of deep networks to learn the distribution of multiple large-scale fields, conditioned exclusively on cosmology while marginalizing over astrophysics. Our approach develops a generalized multifield emulator based purely on theoretical predictions from the state-of-the-art hydrodynamic simulations of the CAMELS project, without incorporating instrumental effects which limit the analysis to specifics of a particular large-scale survey design. To this end, we train a generative adversarial network to generate images composed of three different channels that represent gas density (Mgas), neutral hydrogen density (HI), and magnetic field amplitudes (B). We consider an unconstrained model and another scenario where the model is conditioned on the matter density \(\Omega _{\mathrm{m}}\) and the amplitude of density fluctuations \(\sigma _{8}\). We find that the generated images exhibit great quality which is on a par with that of data, visually. Quantitatively, we find that our model generates maps whose statistical properties, quantified by probability distribution function (PDF) of pixel values and auto-power spectra, agree reasonably well up to the second moment with those of the real maps. The relative deviation between the PDFs is about 25\(\%\) in both moments with larger deviations at the tails. The error between the two auto-power spectra is approximately less than 20\(\%\) on scales larger than \(k = 10 h/\)Mpc, but becomes larger on smaller scales. Moreover, the mean and standard deviation of the cross-correlations between fields in all maps produced by the emulator are in good agreement with those of the real images, which indicates that our model generates instances whose maps in all three channels describe the same physical region. Furthermore, a CNN regressor, which has been trained to extract \(\Omega _{\mathrm{m}}\) and \(\sigma _{8}\) from CAMELS multifield dataset, recovers the cosmology from the maps generated by our conditional model, achieving coefficient of determination values \(R^{2} = 0.96\) and 0.83 corresponding to \(\Omega _{\mathrm{m}}\) and \(\sigma _{8}\) respectively. This further demonstrates the great capability of the model to mimic CAMELS data. Our model can be useful for generating data, 1000 multiple images in ∼3 seconds as opposed to a simulation which takes days for one realization, that are required to analyze the information from upcoming multi-wavelength cosmological surveys.