Related papers: Reconstructing the Hubble diagram of gamma-ray bur…
We present a new procedure rooted in deep learning to construct science images from data cubes collected by astronomical instruments using HxRG detectors in low-flux regimes. It improves on the drawbacks of the conventional algorithms to…
Recently, some theoretical models predicted that a small fraction of fast radio bursts (FRBs) could be associated with gravitational waves (GWs). In this work, we discuss the possibility of using GW/FRB association systems, if commonly…
Gamma-ray bursts (GRBs) are the most luminous explosions and can be detectable out to the edge of Universe. It has long been thought they can extend the Hubble diagram to very high redshifts. Several correlations between temporal or…
The identification of $\gamma$-rays from the predominant hadronic-background is a key aspect in their ground-based detection using Imaging Atmospheric Cherenkov Telescopes (IACTs). While current methods are limited in their ability to…
Searching for Lorentz invariance violation (LIV) using astrophysical sources such as gamma-ray bursts (GRBs) is crucial for probing quantum gravity. However, the dependence of LIV constraints on assumed cosmological models has been largely…
In this paper, we examine the spacial distribution of gamma-ray bursts (GRBs) using a sample of 373 objects. We subdivide the GRB data into two redshift intervals over the redshift range $0<z< 6.7$. We measure the two-point correlation…
Fast radio bursts (FRBs) are among the most mysterious astronomical transients. Due to their short durations and cosmological distances, their dispersion measure (DM) - redshift ($z$) relation is useful for constraining cosmological…
We put forward a new model-independent reconstruction scheme for dark energy which utilises the expected geometrical features of the luminosity-distance relation. The important advantage of this scheme is that it does not assume explicit…
The spatial curvature of the Universe remains a central question in modern cosmology. In this work, we explore the potential of localized Fast Radio Bursts (FRBs) as a novel tool to constrain the cosmic curvature parameter $\Omega_k$ in a…
While Gamma-Ray Burst (GRBs) are clear and distinct observed events, every individual GRB is unique. In fact, GRBs are known for their variable behaviour, and BATSE was already able to discover two categories of GRB from the T90…
The current accelerated expansion of the Universe remains ones of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced…
This paper presents a learning-based method for calibrating and denoising microelectromechanical system (MEMS) gyroscopes, which is designed based on a convolutional network, and only contains hundreds of parameters, so the network can be…
The new generation of deep photometric surveys requires unprecedentedly precise shape and photometry measurements of billions of galaxies to achieve their main science goals. At such depths, one major limiting factor is the blending of…
Due to the lack of low redshift long Gamma-Ray Bursts (GRBs), the circular problem has been a severe obstacle for using GRBs as cosmological candles. In this paper, we present a new method to deal with such a problem in MCMC global fitting…
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…
We present the constraints on the cosmological parameters obtained with the $E_{\rm peak}$--$E_{\gamma}$ correlation found with the most recent sample of 19 GRBs with spectroscopically measured redshift and well determined prompt emission…
Line intensity mapping is emerging as a novel method that can measure the collective intensity fluctuations of atomic/molecular line emission from distant galaxies. Several observational programs with various wavelengths are ongoing and…
This article presents a physics-informed deep learning method for the quantitative estimation of the spatial coordinates of gamma interactions within a monolithic scintillator, with a focus on Positron Emission Tomography (PET) imaging. A…
We present a decentralized model for autonomous reconfiguration of homogeneous pivoting cube modular robots in two dimensions. Each cube in the ensemble is controlled by a neural network that only gains information from other cubes in its…
We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and…