Optimal Coaddition of Imaging Data for Rapidly Fading Gamma-Ray Burst Afterglows

TL;DR

This paper introduces an optimal coaddition method for imaging data of rapidly fading gamma-ray burst afterglows, improving detection probability by weighting exposures based on lightcurve and noise characteristics.

Contribution

It presents a novel optimal weighting technique for coadding images of rapidly fading sources, enhancing signal-to-noise ratio and detection chances over traditional unweighted methods.

Findings

Optimal coaddition increases detection probability of GRB afterglows.

Method improves S/N ratio especially for high decay rates and low source counts.

Application to Swift UVOT data demonstrates practical effectiveness.

Abstract

We present a technique for optimal coaddition of image data for rapidly varying sources, with specific application to gamma-ray burst (GRB) afterglows. Unweighted coaddition of rapidly fading afterglow lightcurve data becomes counterproductive relatively quickly. It is better to stop coaddition of the data once noise dominates late exposures. A better alternative is to optimally weight each exposure to maximize the signal-to-noise ratio (S/N) of the final coadded image data. By using information about GRB lightcurves and image noise characteristics, optimal image coaddition increases the probability of afterglow detection and places the most stringent upper limits on non-detections. For a temporal power law flux decay typical of GRB afterglows, optimal coaddition has the greatest potential to improve the S/N of afterglow imaging data (relative to unweighted coaddition), when the decay rate is high, the source count rate is low, and the background rate is high. The optimal coaddition technique is demonstrated with applications to Swift Ultraviolet/Optical Telescope (UVOT) data of several GRBs, with and without detected afterglows.