Title:	Gamma-ray bursts
DNr:	SNIC 2016/1-223
Project Type:	SNIC Medium Compute
Principal Investigator:	Josefin Larsson <josla@kth.se>
Affiliation:	Kungliga Tekniska högskolan
Duration:	2016-05-01 – 2017-05-01
Classification:	10305
Keywords:

Abstract

Gamma-ray bursts are the brightest explosions in the universe. During a brief period of time, typically lasting seconds to minutes, they emit as much energy as the Sun does during its entire life time. The gamma-ray emission originates from a so called jet, which is a collimated beam of plasma travelling close to the speed of light. The jets are believed to form when massive stars explode and when compact objects merge. Understanding gamma-ray bursts has implications for a range of important topics, including black hole formation and particle acceleration. Since they can be seen at great distances, gamma-ray bursts also serve as excellent probes of the early universe. A long-standing problem in the field is to understand the radiative processes that give rise to the gamma-ray emission. An answer to this question is needed in order to be able to derive many of the most important physical properties of the systems. In the past progress has been hindered by lack of physical models with which to analyse data. We have recently made a significant advancement in this respect by using a code that treats all the relevant radiation processes in a relativistic outflow. Specifically, we have run the code for a range of input parameters, thus producing a grid of spectra corresponding to different physical conditions. We then fit observational data of gamma-ray bursts by interpolating in this grid. Our initial results (presented in Ahlgren at al., 2015, MNRAS, 454, 31) show excellent fits as well as good constraints on physical parameters. With this proposal our aim is to significantly extend the grid by performing more simulations, thus covering a larger parameter space. This will increase the amount of information that can be extracted from the data analysis. We started this work with our previous allocation, but much of the parameter space remains unexplored. With the requested resources we expect to cover the parameter space needed in order to construct significantly improved models that can be used to analyse data.