Related papers: Model independent calibrations of gamma ray bursts…
We study eight different gamma-ray burst (GRB) data sets to examine whether current GRB measurements -- that probe a largely unexplored part of cosmological redshift ($z$) space -- can be used to reliably constrain cosmological model…
Cluster analysis is a widely applied machine learning technique to understand the existing patterns in the population of gamma-ray bursts (GRBs), in order to explore their physical sources. In the present scenario, the number of clusters…
Model miscalibration has been frequently identified in modern deep neural networks. Recent work aims to improve model calibration directly through a differentiable calibration proxy. However, the calibration produced is often biased due to…
We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clusters via the Gaussian mixture model (GMM)…
In this paper, we use two model-independent methods to standardize long gamma-ray bursts (GRBs) using the $E_{\rm iso}-E_{\rm p}$ correlation, where $E_{\rm iso}$ is the isotropic-equivalent gamma-ray energy and $E_{\rm p}$ is the spectral…
Recently, both theoretical arguments and observational evidence suggested that a small fraction of fast radio bursts (FRBs) could be associated with gamma-ray bursts (GRBs). If such FRB/GRB association systems are commonly detected in the…
Using gamma-ray bursts as standard candles for cosmological parameter constraints rely on their empirical luminosity relations and low-redshift calibration. In this paper, we examine the Amati relation and its potential corrections based on…
Gamma-ray bursts (GRBs) are among the most powerful sources in the universe. In the recent years, GRBs have been proposed as a complementary probe to type Ia supernovae (SNIa). However, as is well known, there is a circularity problem in…
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator.…
Looking at the well-known Hubble tension as a tension in the calibrators of the cosmic distance ladder, i.e. the absolute magnitude $M$ of standard candles such as supernovae of Type Ia (SNIa) and the standard ruler represented by the…
For the use of Gamma-Ray Bursts (GRBs) to probe cosmology in a cosmology-independent way, a new method has been proposed to obtain luminosity distances of GRBs by interpolating directly from the Hubble diagram of SNe Ia, and then…
Gaussian graphical model selection is usually studied under independent sampling, but in many applications observations arise from dependent dynamics. We study structure learning when the data consist of a single trajectory of Gaussian…
Different models of dark matter can alter the distribution of mass in galaxy clusters in a variety of ways. However, so can uncertain astrophysical feedback mechanisms. Here we present a Machine Learning method that ''learns'' how the…
A method for correcting smearing effects using machine learning technique is presented. Compared to the standard deconvolution approaches in high energy particle physics, the method can use more than one reconstructed variable to predict…
Gamma-ray bursts (GRBs) have been regarded as standard candles at very high redshift for cosmology research. We have proposed a new method to calibrate GRB distance indicators with Type Ia supernova (SNe Ia) data in a completely…
Upcoming 21cm surveys will map the spatial distribution of cosmic neutral hydrogen (HI) over unprecedented volumes. Mock catalogues are needed to fully exploit the potential of these surveys. Standard techniques employed to create these…
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…
We propose a machine learning-based approach for parameter estimation of Massive Black Hole Binaries (MBHBs), leveraging normalizing flows to approximate the likelihood function. By training these flows on simulated data, we can generate…
The increase in the observed volume in cosmological surveys imposes various challenges on simulation preparations. Firstly, the volume of the simulations required increases proportionally to the observations. However, large-volume…
Several gamma-ray burst (GRB) luminosity indicators (LIs)have been proposed. Unlike SNe Ia, calibration of GRB LIs using a low-redshift sample is difficult. Based on the Bayesian theory, here we propose an approach to calibrate these LIs…