Automatic analysis of Swift-XRT data

TL;DR

This paper presents automated methods for rapid and accurate analysis of Swift-XRT data to improve GRB position accuracy and generate high-quality X-ray light curves, aiding follow-up observations.

Contribution

It introduces fully automated techniques for swift analysis of XRT data, enhancing the speed and quality of GRB localization and light curve production.

Findings

Improved accuracy of GRB positions from XRT data.

Automated generation of science-grade X-ray light curves.

Rapid data processing enables timely follow-up observations.

Abstract

The Swift spacecraft detects and autonomously observes ~100 Gamma Ray Bursts (GRBs) per year, ~96% of which are detected by the X-ray telescope (XRT). GRBs are accompanied by optical transients and the field of ground-based follow-up of GRBs has expanded significantly over the last few years, with rapid response instruments capable of responding to Swift triggers on timescales of minutes. To make the most efficient use of limited telescope time, follow-up astronomers need accurate positions of GRBs as soon as possible after the trigger. Additionally, information such as the X-ray light curve, is of interest when considering observing strategy. The Swift team at Leicester University have developed techniques to improve the accuracy of the GRB positions available from the XRT, and to produce science-grade X-ray light curves of GRBs. These techniques are fully automated, and are executed as soon as data are available.