Robust photometric redshift determinations of gamma-ray burst afterglows at z > 2

TL;DR

This paper presents GRBz, a novel method for early photometric redshift estimation of gamma-ray burst afterglows using multi-parameter fitting of optical and near-infrared data, accounting for absorption effects and host extinction.

Contribution

Introduces GRBz, a genetic algorithm-based code for simultaneous fitting of GRB afterglow parameters to determine redshift, spectral index, and host extinction from photometric data.

Findings

Effective in estimating GRB redshifts with minimal bias.

Proven accuracy using GRBs with known spectroscopic redshifts.

Incorporates Monte Carlo analysis for reliable error estimation.

Abstract

Theory suggests that about 10% of Swift-detected gamma-ray bursts (GRBs) will originate at redshifts greater than 5 yet a number of high redshift candidates may be left unconfirmed due to the lack of measured redshifts. Here we introduce our code, GRBz, a method of simultaneous multi-parameter fitting of GRB afterglow optical and near infrared, spectral energy distributions. It allows for early determinations of the photometric redshift, spectral index and host extinction to be made. We assume that GRB afterglow spectra are well represented by a power-law decay and model the effects of absorption due to the Lyman forest and host extinction. We use a genetic algorithm-based routine to simultaneously fit the parameters of interest, and a Monte Carlo error analysis. We use GRBs of previously determined spectroscopic redshifts to prove our method, while also introducing new near infrared data of GRB 990510 which further constrains the value of the host extinction. Our method is effective in estimating the photometric redshift of GRBs, relatively unbiased by assumptions of the afterglow spectral index or the host galaxy extinction. Monte Carlo error analysis is required as the method of error estimate based on the optimum population of the genetic algorithm underestimates errors significantly.