Direction Dependent Background Fitting for the Fermi GBM Data

TL;DR

This paper introduces a Direction Dependent Background Fitting method for Fermi GBM data that improves background estimation by accounting for satellite motion, especially useful for long GRBs and variable backgrounds.

Contribution

The paper presents a novel physical model-based background fitting method that outperforms polynomial fitting, enabling analysis of long-duration and variable-background GRBs.

Findings

Successfully applied to nine GRBs, including ARR events.

Able to fit long background intervals and remove satellite motion effects.

Automated parts of the fitting process for both Sky Survey and ARR modes.

Abstract

We present a method for determining the background of Fermi GBM GRBs using the satellite positional information and a physical model. Since the polynomial fitting method typically used for GRBs is generally only indicative of the background over relatively short timescales, this method is particularly useful in the cases of long GRBs or those which have Autonomous Repoint Request (ARR) and a background with much variability on short timescales. We give a Direction Dependent Background Fitting (DDBF) method for separating the motion effects from the real data and calculate the duration (T90 and T50, as well as confidence intervals) of the nine example bursts, from which two resulted an ARR. We also summarize the features of our method and compare it qualitatively with the official GBM Catalogue. Our background filtering method uses a model based on the physical information of the satellite position. Therefore, it has many advantages compared to previous methods. It can fit long background intervals, remove all the features caused by the rocking behaviour of the satellite, and search for long emissions or not-triggered events. Furthermore, many part of the fitting have now been automatised, and the method have been shown to work for both Sky Survey mode and ARR mode data. Future work will provide a burst catalogue with DDBF.