Related papers: Reconstructing Lyman-$\alpha$ Fields from Low-Reso…
Cosmological hydrodynamic simulations can accurately predict the properties of the intergalactic medium (IGM), but only under the condition of retaining high spatial resolution necessary to resolve density fluctuations in the IGM. This…
We extend our super-resolution and emulation framework for cosmological dark matter simulations to include hydrodynamics. We present a two-stage deep learning model to emulate high-resolution (HR-HydroSim) baryonic fields from…
The next generation of cosmological spectroscopic sky surveys will probe the distribution of matter across several Gigaparsecs (Gpc) or many billion light-years. In order to leverage the rich data in these new maps to gain a better…
Full-physics cosmological simulations are powerful tools for studying the formation and evolution of structure in the universe but require extreme computational resources. Here, we train a convolutional neural network to use a cheaper…
The inference of cosmological quantities requires accurate and large hydrodynamical cosmological simulations. Unfortunately, their computational time can take millions of CPU hours for a modest coverage in cosmological scales ($\approx (100…
We introduce an efficient and accurate alternative to full hydrodynamic simulations, Hydro-PM (HPM), for the study of the low column density Lyman-alpha forest. It consists of a Particle-Mesh solver, modified to compute, in addition to the…
Lyman-$\alpha$(Ly$\alpha$) forest in the spectra of distant quasars encodes the information of the underlying cosmic density field at smallest scales. The modelling of the upcoming large and high-fidelity forest data using cosmological…
Numerical hydrodynamical simulations are used to predict the expected absorption properties of the Lyman-Alpha forest for a variety of Cold Dark Matter dominated cosmological scenarios: CHDM, OCDM, LCDM, SCDM, and tCDM. Synthetic spectra…
We analyze the flux power spectrum and its covariance using simulated Lyman alpha forests. We find that pseudo-hydro techniques are good approximations of hydrodynamical simulations at high redshift. However, the pseudo-hydro techniques…
Cosmological information is usually extracted from the Lyman-$\alpha$ forest correlations using only either large-scale information interpreted through linear theory or using small-scale information interpreted by means of expensive…
Numerical hydrodynamical simulations have proven a successful means of reproducing many of the statistical properties of the Lyman-Alpha forest as measured in high redshift quasar spectra. Pseudo-hydrodynamical methods based only on…
Hydrodynamical simulations are the most accurate way to model structure formation in the universe, but they often involve a large number of astrophysical parameters modeling subgrid physics, in addition to cosmological parameters. This…
Cosmological field-level inference requires differentiable forward models that solve the challenging dynamics of gas and dark matter under hydrodynamics and gravity. We propose a hybrid approach where gravitational forces are computed using…
We explore the use of Deep Learning to infer physical quantities from the observable transmitted flux in the Lyman-alpha forest. We train a Neural Network using redshift z=3 outputs from cosmological hydrodynamic simulations and mock…
We study the statistics of the Lyman-$\alpha$ forest in a flat LCDM cosmology with the N-body + Eulerian hydrodynamics code Nyx. We produce a suite of simulations, covering the observationally relevant redshift range $2 \leq z \leq 4$. We…
Hydrodynamic simulations are powerful tools for studying galaxy formation. However, it is crucial to test and improve the sub-grid physics underlying these simulations by comparing their predictions with observations. To this aim,…
The signature left in quasar spectra by the presence of neutral hydrogen in the Universe allows one to constrain the sum of the neutrino masses with improved sensitivity, with respect to laboratory experiments, and may shed a new light on…
We test a method to reduce unwanted sample variance when predicting Lyman-$\alpha$ (ly$\alpha$) forest power spectra from cosmological hydrodynamical simulations. Sample variance arises due to sparse sampling of modes on large scales and…
High-fidelity modeling of turbulent flows is one of the major challenges in computational physics, with diverse applications in engineering, earth sciences and astrophysics, among many others. The rising popularity of high-fidelity…
We aim to construct a machine-learning approach that allows for a pixel-by-pixel reconstruction of the intergalactic medium (IGM) density field for various warm dark matter (WDM) models using the Lyman-alpha forest. With this regression…