Related papers: Advancing Gamma-Ray Burst Identification through T…
The division of Gamma-ray bursts (GRBs) into different classes, other than the "short" and "long", has been an active field of research. We investigate whether GRBs can be classified based on a broader set of parameters, including prompt…
We exploit the 14 gamma-ray bursts (GRBs) with known redshifts $z$ and the 7 GRBs for which there are constraints on $z$ to determine the GRB rate $R_{\rm GRB}(z)$, using a method based on Bayesian inference. We find that, despite the…
Gamma Ray Bursts (GRBs) are a powerful probe of the high redshift Universe. We present a tool to estimate the detection rate of high-z GRBs by a generic detector with defined energy band and sensitivity. We base this on a population model…
Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical…
Photoexcitation and ionization of partially ionized heavy atoms in highly relativistic flows by interstellar photons, followed by their reemission in radiative recombination and decay, boost star-light into beamed $\gamma$ rays along the…
Gamma-ray bursts (GRBs) were first detected thanks to their prompt emission, which was the only information available for decades. In 2010, while the high-energy prompt emission remains the main tool for the detection and the first…
Gamma-ray bursts (GRBs) offer a powerful probe of the cosmic expansion history far beyond the redshift range accessible to Type Ia supernovae. However, the study of cosmological models using GRBs is hindered by the circularity problem,…
Gamma-ray bursts (GRBs) are short and most intense bursts of gamma-rays that come from random direction in space. Their origin are still unknown and they originate likely from cosmological distances, probably after birth of a new black hole…
Gamma-Ray Bursts (GRBs) are the most violent explosions in the Universe. Long duration GRBs are associated with the collapse of massive stars, rivaling their host galaxies in luminosity. The discovery of the most distant spectroscopically…
Gamma-Ray Bursts (GRBs), being observed at high redshift (z = 9.4), vital to cosmological studies and investigating Population III stars. To tackle these studies, we need correlations among relevant GRB variables with the requirement of…
Raman spectroscopy is a powerful analytical tool with applications ranging from quality control to cutting edge biomedical research. One particular area which has seen tremendous advances in the past decade is the development of powerful…
We use a new method of analysis to determine parameters of cosmological gamma-ray bursts (GRBs), assuming that their distribution follows the star-formation history of the universe. Spectral evolution is calculated from an external shock…
We calibrate the distance and reconstruct the Hubble diagram of gamma-ray bursts (GRBs) using deep learning. We construct an artificial neural network, which combines the recurrent neural network and Bayesian neural network, and train the…
Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems. However, these deep models are perceived as "black box" methods considering the lack of understanding of their…
The Fermi Gamma-ray Burst Monitor (GBM) has detected over 1400 Gamma-Ray Bursts (GRBs) since it began science operations in July, 2008. We use a subset of over 300 GRBs localized by instruments such as Swift, the Fermi Large Area Telescope,…
Deep learning techniques, namely convolutional neural networks (CNN), have previously been adapted to select gamma-ray events in the TAIGA experiment, having achieved a good quality of selection as compared with the conventional Hillas…
Since their first discovery in the late 1960s, Gamma-ray bursts have attracted an exponentially growing interest from the international community due to their central role in the most highly debated open questions of the modern research of…
In this paper, a performance evaluation of well-known deep learning models in gait recognition is presented. For this purpose, the transfer learning scheme is adopted to pre-trained models in order to fit the models to the CASIA-B dataset…
We present a statistical method based on scDEED to assess the reliability of a 2D embedding showing a low-dimensional representation of the distribution of Gamma-Ray Bursts (GRBs) detected by the Fermi Gamma-ray Burst Monitor (GBM). The…
We exploit the 14 gamma-ray bursts (GRBs) with known redshifts z and the 7 GRBs for which there are constraints on z to determine the GRB rate R_{GRB}(z), using a method based on Bayesian inference. We find that, despite the qualitative…