Fitting Afterglows With Multi-Dimensional Simulations

TL;DR

This paper introduces ScaleFit, a Python tool that uses multi-dimensional simulations and MCMC to efficiently fit gamma-ray burst afterglow light curves, revealing key physical parameters and observational biases.

Contribution

The paper presents a novel, fast fitting method combining hydrodynamic simulations and scaling relations, enabling detailed analysis of afterglow light curves with minimal computational effort.

Findings

ScaleFit accurately measures jet and observer angles from Swift-XRT data.

Most gamma-ray burst afterglows are observed off-axis.

The method significantly reduces fitting time for complex simulations.

Abstract

We present preliminary data fit results of synthetic light curves computed from numerical afterglow blast wave simulations. Our technique uses Markov chain Monte Carlo (MCMC) in a new data analysis tool, ScaleFit. Scaling relations in both the hydrodynamics and radiation equations allow light curves to be parameterized by a small set of scale-invariant characteristic quantities. These quantities have been calculated and tabulated from high resolution two-dimensional hydrodynamic simulations. Producing a light curve from the characteristics takes only a millisecond, allowing for the use of MCMC data fitting techniques which can require millions of iterations. ScaleFit is a portable, lightweight, python package which performs this analysis on afterglow light curves. Using the set of Swift-XRT light curves from 2011 & 2012 with known redshifts, we find ScaleFit can measure the jet opening angle, observer angle, and spectral index of most afterglows. Globally we find gamma-ray burst afterglows tend to be observed off axis, at a significant fraction of the jet opening angle.