Nonlinear 3D cosmic web simulation with heavy-tailed generative adversarial networks

Fast and accurate simulations of the nonlinear evolution of the cosmic density field are a major component of many cosmological analyses, but the computational time and storage required to run them can be exceedingly large. For this reason, we use generative adversarial networks (GANs) to learn a compressed representation of the 3D matter density field that is fast and easy to sample, and for the first time show that GANs are capable of generating samples at the level of accuracy of other conventional methods. Using subvolumes from a suite of GADGET-2 N-body simulations, we demonstrate that a deep-convolutional GAN can generate samples that capture both large- and small-scale features of the matter density field, as validated through a variety of n-point statistics. The use of a data scaling that preserves high-density features and a heavy-tailed latent space prior allow us to obtain state of the art results for fast 3D cosmic web generation. In particular, the mean power spectra from generated samples agree to within 5% up to k=3 and within 10% for k≤5 when compared with N-body simulations, and similar accuracy is obtained for a variety of bispectra. By modeling the latent space with a heavy-tailed prior rather than a standard Gaussian, we better capture sample variance in the high-density voxel PDF and reduce errors in power spectrum and bispectrum covariance on all scales. Furthermore, we show that a conditional GAN can smoothly interpolate between samples conditioned on redshift. Deep generative models, such as the ones described in this work, provide great promise as fast, low-memory, high-fidelity forward models of large-scale structure

Nonlinear 3D Cosmic Web Simulation with Heavy-Tailed Generative Adversarial Networks

Fast and accurate simulations of the non-linear evolution of the cosmic density field are a major component of many cosmological analyses, but the computational time and storage required to run them can be exceedingly large. For this reason, we use generative adversarial networks (GANs) to learn a compressed representation of the 3D matter density field that is fast and easy to sample, and for the first time show that GANs are capable of generating samples at the level of accuracy of other conventional methods. Using sub-volumes from a suite of GADGET-2 N-body simulations, we demonstrate that a deep-convolutional GAN can generate samples that capture both large- and small-scale features of the matter density field, as validated through a variety of n-point statistics. The use of a data scaling that preserves high-density features and a heavy-tailed latent space prior allow us to obtain state of the art results for fast 3D cosmic web generation. In particular, the mean power spectra from generated samples agree to within 5% up to k=3 and within 10% for k<5 when compared with N-body simulations, and similar accuracy is obtained for a variety of bispectra. By modeling the latent space with a heavy-tailed prior rather than a standard Gaussian, we better capture sample variance in the high-density voxel PDF and reduce errors in power spectrum and bispectrum covariance on all scales. Furthermore, we show that a conditional GAN can smoothly interpolate between samples conditioned on redshift. Deep generative models, such as the ones described in this work, provide great promise as fast, low-memory, high-fidelity forward models of large-scale structure.Comment: 19 pages, 17 figures. v3: Reflects changes in version published in PR

Deep neural networks to unveil the properties of the cosmic web

The main goal of this Thesis work is to test Machine Learning techniques for cosmological analyses. We develop and validate new methods and numerical algorithms to constrain the main parameters of the standard cosmological model, that is Ωm, Ωb, h, ns, σ8, exploiting a likelihood-free inference analysis. The training dataset considered in this work consists of a huge set of second-order and third-order statistics of the dark matter density field, measured from the Quijote N-body simulations [Villaescusa-Navarroet al., 2019]. These are one of the largest sets of dark matter N-body simulations currently available, that span a significant range of the cosmological parameters of the standard model. We implement and train new Neural Networks that can take in input measurements of two-point correlation functions, power spectra and bispectra, and provide in output constraints on the main cosmological parameters. After the training and validation phases, we test the accuracy of our implemented Machine Learning algorithms by processing never-seen-before input datasets generated with cosmological parameters comparable with Planck18 ones [Planck Collaboration et al., 2018]. We find that this statistical procedure can provide robust constraints on some of the aforementioned parameters, in particular Ωm. This Thesis work demonstrates that the considered deep learning techniques based on state-of-the-art Artificial Neural Networks can be effectively employed in cosmological studies, in particular to constrain the main parameters of the cosmological framework by exploiting the statistics of the large-scale structure of the Universe