相关论文: Reconstructing the Hubble diagram of gamma-ray bur…

Gamma-ray bursts (GRBs) are intense, short-lived bursts of gamma-ray radiation observed up to a high redshift ($z \sim 10$) due to their luminosities. Thus, they can serve as cosmological tools to probe the early Universe. However, we need…

A new method of measuring cosmology with gamma-ray bursts(GRBs) has been proposed by Liang and Zhang recently. In this method, only observable quantities including the rest frame peak energy of the \nu F_\nu spectrum (E'_p), the isotropic…

We present a new method aimed to handle long Gamma-Ray Burst (GRBs) as cosmic rulers. The recent discovery of a tight correlation between the collimation corrected GRB energy and the peak of the gamma-ray spectrum has opened the possibility…

We combine recent long Gamma Ray Bursts (GRBs) sample including 52 objects out to z$=$6.3 compiled from Swift Gamma Ray Bursts by Schaefer (2006) with Type Ia Supernova (SNIa), Cosmic Microwave Background (CMB) and Baryon Oscillation (BAO)…

Cosmological probes at any redshift are necessary to reconstruct consistently the cosmic history. Studying properly the tension on the Hubble constant, $H_0$, obtained by Supernovae Type Ia (SNe Ia) and the Planck measurements of the Cosmic…

Multi-messenger (MM) observations of binary neutron star (BNS) mergers provide a promising approach to trace the distance-redshift relation, crucial for understanding the expansion history of the Universe and, consequently, testing the…

A new method to constrain the cosmological equation of state is proposed by using combined samples of gamma-ray bursts (GRBs) and supernovae (SNeIa). The Chevallier-Polarski-Linder parameterization is adopted for the equation of state in…

We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference. We employ Graph Neural Networks (GNNs), architectures designed to…

We use the measurement of gamma-ray burst (GRB) distances to constrain dark energy cosmological model parameters. We employ two methods for analyzing GRB data - fitting luminosity relation of GRBs in each cosmology and using distance…

Next-generation cosmic microwave background (CMB) surveys are expected to provide valuable information about the primordial universe by creating maps of the mass along the line of sight. Traditional tools for creating these lensing…

One of the few ways to measure the properties of Dark Energy is to extend the Hubble daigram (HD) to higher redshifts with Gamma-Ray Bursts (GRBs). GRBs have at least five properties (their spectral lag, variability, spectral peak photon…

The LambdaCDM model is the most commonly accepted framework in modern cosmology. However, the local measurements of the Hubble constant, H0, via the Supernovae Type Ia (SNe Ia) calibrated on Cepheids provide a value which is in significant…

The Rapid and accurate identification of Gamma-Ray Bursts (GRBs) is crucial for unraveling their origins. However, current burst search algorithms frequently miss low-threshold signals or lack universality for observations. In this study,…

We examine the prospects of the high redshift Long Gamma Ray Bursts (LGRB) Hubble Diagram as a test of the basic cosmological principles. Analysis of the Hubble Diagram allows us to test several fundamental cosmological principles using the…

We present track reconstruction algorithms based on deep learning, tailored to overcome specific central challenges in the field of hadron physics. Two approaches are used: (i) deep learning (DL) model known as fully-connected neural…

In this work, our focus is on exploring the potential of current GRB measurements to provide reliable constraints on cosmological model parameters at high redshift. This work is divided into two parts. First, we calibrate the Amati relation…

Combining the `time-delay distance' ($D_{\Delta t}$) measurements from galaxy lenses and other distance indicators provides model-independent determinations of the Hubble constant ($H_0$) and spatial curvature ($\Omega_{K,0}$), only based…

We examine the use of a novel variant of Physics-Informed Neural Networks to predict cosmological parameters from recent supernovae and baryon acoustic oscillations (BAO) datasets. Our machine learning framework generates uncertainty…

Gamma-ray bursts (GRBs) can be probes of the early universe, but currently, only 26% of GRBs observed by the Neil Gehrels Swift Observatory GRBs have known redshifts ($z$) due to observational limitations. To address this, we estimated the…

We present a convolutional neural network to classify distinct cosmological scenarios based on the statistically similar weak-lensing maps they generate. Modified gravity (MG) models that include massive neutrinos can mimic the standard…