An analysis of the effect of data processing methods on magnetic propeller models in short GRBs

TL;DR

This study examines how different data processing methods affect the analysis of short gamma-ray burst light-curves and their modeling, emphasizing the importance of error propagation in physical parameter estimation.

Contribution

It demonstrates the impact of error propagation from count-rate-to-flux conversion on light-curve morphology and model parameters in SGRBEEs analysis.

Findings

Light-curve morphologies differ across energy bands due to data processing.

Error propagation affects the constraints on physical parameters.

Model consistency varies with data processing methods.

Abstract

We present analysis of observational data from the Swift Burst Analyser for a sample of 15 short gamma-ray bursts with extended emission (SGRBEEs) which have been processed such that error propagation from Swift's count-rate-to-flux conversion factor is applied to the flux measurements. We apply this propagation to data presented by the Burst Analyser at 0.3-10 keV and also at 15-50 keV, and identify clear differences in the morphologies of the light-curves in the different bands. In performing this analysis with data presented at both 0.3-10 keV, at 15-50 keV, and also at a combination of both bands, we highlight the impact of extrapolating data from their native bandpasses on the light-curve. We then test these data by fitting to them a magnetar-powered model for SGRBEEs, and show that while the model is consistent with the data in both bands, the model's derived physical parameters are generally very loosely constrained when this error propagation is included and are inconsistent across the two bands. In this way, we highlight the importance of the Swift data processing methodology to the details of physical model fits to SGRBEEs.