Related papers: Large, fast and accurate HI intensity maps with la…
Upcoming 21cm surveys with the SKA1-LOW telescope will enable imaging of the neutral hydrogen distribution on cosmological scales in the early Universe. These surveys are expected to generate huge imaging datasets that will encode more…
We report the results of the first study of the multi-stream environment of dark matter haloes in cosmological N-body simulations in the LCDM cosmology. The full dynamical state of dark matter can be described as a three-dimensional…
The main goal of this analysis is to present a new method to estimate the physical properties of diffuse cloud of atomic hydrogen observed at high Galactic latitude. This method, based on a comparison of the observations with fractional…
The high-redshift 21-cm signal of neutral hydrogen is expected to be observed within the next decade and will reveal epochs of cosmic evolution that have been previously inaccessible. Due to the lack of observations, many of the…
We present a generalization of our recently proposed machine learning framework, aiming to provide new physical insights into dark matter halo formation. We investigate the impact of the initial density and tidal shear fields on the…
Mock halo catalogues are indispensable data products for developing and validating cosmological inference pipelines. A major challenge in generating mock catalogues is modelling the halo or galaxy bias, which is the mapping from matter…
We present a new algorithm for identifying dark matter halos, substructure, and tidal features. The approach is based on adaptive hierarchical refinement of friends-of-friends groups in six phase-space dimensions and one time dimension,…
Context. Building on the transformative success of optical redshift surveys, the emerging technique of neutral hydrogen (HI) intensity mapping (IM) offers a novel probe of large-scale structure (LSS) growth and the late-time accelerated…
In this paper, as the third part of the third step of our study on developing data analysis procedures for using 3-dimensional information offered by directional direct Dark Matter detection experiments in the future, we introduce a…
HI intensity mapping is a new observational technique to map fluctuations in the large-scale structure of matter using the 21 cm emission line of atomic hydrogen (HI). Sensitive radio surveys have the potential to detect Baryon Acoustic…
The scale radius of dark matter halos is a critical parameter for specifying the density distribution of dark matter, and is therefore a fundamental parameter for modeling galaxies. We develop here a novel, observationally motivated probe…
We study the phase-space structure of a dark-matter halo formed in a high resolution simulation of a Lambda CDM cosmology. Our goal is to quantify how much substructure is left over from the inhomogeneous growth of the halo, and how it may…
In the era of precision cosmology, the ability to generate accurate and large-scale galaxy catalogs is crucial for advancing our understanding of the universe. With the flood of cosmological data from current and upcoming missions,…
High-resolution image synthesis remains a core challenge in generative modeling, particularly in balancing computational efficiency with the preservation of fine-grained visual detail. We present Latent Wavelet Diffusion (LWD), a…
The redshifted 21-cm signal emitted by neutral Hydrogen (HI) is a promising probe to understand the evolution of the topology of ionized regions during the Epoch of Reionization (EoR). The topology of ionized regions allows us to infer the…
Modern N-body cosmological simulations contain billions ($10^9$) of dark matter particles. These simulations require hundreds to thousands of gigabytes of memory, and employ hundreds to tens of thousands of processing cores on many compute…
The epoch of reionization is one of the least known chapters in the evolutionary history of the Universe. This thesis investigates two major approaches to unveil the reionization history of the Universe using HI 21-cm maps.The most…
Herein, we present a deep-learning technique for reconstructing the dark-matter density field from the redshift-space distribution of dark-matter halos. We built a UNet-architecture neural network and trained it using the COmoving…
We propose a deep learning analyzing technique with convolutional neural network (CNN) to predict the evolutionary track of the Epoch of Reionization (EoR) from the 21-cm differential brightness temperature tomography images. We use…
In this work, we investigate the feasibility of using ringdown waveforms from supermassive black holes immersed in dark-matter halos to extract both the intrinsic black-hole parameters and those characterizing the surrounding matter…