Optical Classification of Gamma-Ray Bursts in the Swift Era

TL;DR

This paper introduces a new, less assumption-dependent method for classifying optically dark gamma-ray bursts using Swift data, enabling quick identification of optically faint or bright bursts and clarifying previous correlation misconceptions.

Contribution

The paper presents a novel classification technique based on spectral indices that improves upon prior methods by reducing model dependence and offers a new perspective on optical darkness correlations.

Findings

The new method effectively classifies optically dark GRBs.

It identifies extremely bright optical GRBs.

The optical darkness-X-ray brightness correlation is a selection effect.

Abstract

We propose a new method for the classification of optically dark gamma-ray bursts (GRBs), based on the X-ray and optical-to-X-ray spectral indices of GRB afterglows, and utilizing the spectral capabilities of Swift. This method depends less on model assumptions than previous methods, and can be used as a quick diagnostic tool to identify optically sub-luminous bursts. With this method we can also find GRBs that are extremely bright at optical wavelengths. We show that the previously suggested correlation between the optical darkness and the X-ray/gamma-ray brightness is merely an observational selection effect.