Related papers: Reconstructing Lyman-$\alpha$ Fields from Low-Reso…
Simulation of fluid flow in porous media has many applications, from the micro-scale (cell membranes, filters, rocks) to macro-scale (groundwater, hydrocarbon reservoirs, and geothermal) and beyond. Direct simulation of flow in porous media…
We perform for the first time full simulation-based inference on the Lyman-$\alpha$ forest 1D power spectrum. In particular, we consider the prediction of the Lyman-$\alpha$ forest $P_{\rm 1D}(k)$ at $2.0<z<3.5$ from the CAMELS cosmological…
Recent studies have focused on the low-$z$ Ly$\alpha$ forest as a potential constraint on galactic feedback, as different AGN and stellar feedback models in hydrodynamic simulations produce varying intergalactic medium (IGM) statistics.…
A wealth of cosmological and astrophysical information is expected from many ongoing and upcoming large-scale surveys. It is crucial to prepare for these surveys now and develop tools that can efficiently extract most information. We…
The Lyman-$\alpha$ forest refers to the series of absorption features observed in the spectra of distant quasars that are produced by neutral hydrogen in the intergalactic medium. Observed over a wide range of redshifts with both ground-…
We implement the hydro-PM (HPM) technique (Gnedin & Hui 1998) in the hydrodynamical simulation code GADGET-II and quantify the differences between this approximate method and full hydrodynamical simulations of the Lyman-alpha forest in a…
Galaxy formation depends critically on the physical state of gas in the circumgalactic medium (CGM) and its interface with the intergalactic medium (IGM), determined by the complex interplay between inflows from the IGM and outflows from…
Artificial intelligence techniques are considered an effective means to accelerate flow field simulations. However, current deep learning methods struggle to achieve generalization to flow field resolutions while ensuring computational…
We investigate the properties of Damped Lyman-alpha systems (DLAs) using high-resolution and large box-size cosmological hydrodynamical simulations of a LambdaCDM model. The numerical code used is a modification of GADGET-2 with a self…
The Lyman Alpha damping wing is seen in absorption against the spectra of high-redshift galaxies. Modeling of this wing is a way to measure the volume neutral hydrogen fraction of the Universe, as well as to measure the strength of HI…
Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in Artificial Intelligence (specifically Deep Learning) to address this problem. Neural networks have been…
Cosmological hydrodynamical simulations are rich tools to understand the build-up of stellar mass and angular momentum in galaxies, but require some level of calibration to observations. We compare predictions at $z\sim0$ from the Eagle,…
We compare two state-of-the-art numerical codes to study the overall accuracy in modeling the intergalactic medium and reproducing Lyman-$\alpha$ forest observables for DESI and high-resolution data sets. The codes employ different…
We introduce the cosmological HYPER code based on an innovative hydro-particle-mesh (HPM) algorithm for efficient and rapid simulations of gas and dark matter. For the HPM algorithm, we update the approach of Gnedin & Hui (1998) to expand…
Observations of intergalactic neutral hydrogen can provide a wealth of information about structure and galaxy formation, potentially tracing accretion and feedback processes on Mpc scales. Below a column density of NHI ~ 10^19 cm-2, the…
Is a deep learning model capable of understanding systems governed by certain first principle laws by only observing the system's output? Can deep learning learn the underlying physics and honor the physics when making predictions? The…
[abridged] We describe LyMAS (Ly-alpha Mass Association Scheme), a method of predicting clustering statistics in the Ly-alpha forest on large scales from moderate resolution simulations of the dark matter distribution, with calibration from…
Generative machine learning models have been demonstrated to be able to learn low dimensional representations of data that preserve information required for downstream tasks. In this work, we demonstrate that flow matching based generative…
We investigate the reconstruction of a turbulent flow field in the atmospheric boundary layer from a time series of lidar measurements, using Large-Eddy Simulations (LES) and a 4D-Var data assimilation algorithm. This leads to an…
In preparation for the IGM tomography study by Subaru Prime Focus Spectrograph (PFS) survey and other large future telescopes such as TMT/ELT/GMT, we present the results of our pilot study on Ly$\alpha$ forest and IGM tomography statistics…