Anomalies in low-energy Gamma-Ray Burst spectra with the Fermi Gamma-Ray Burst Monitor

TL;DR

This study systematically searches for low-energy spectral deviations in high-fluence Fermi GRBs, revealing significant anomalies in a subset of bursts that suggest the presence of additional spectral components beyond the standard Band function.

Contribution

It introduces a rigorous, blind extrapolated fit method to detect low-energy spectral deviations in Fermi GRB data, demonstrating the frequent occurrence of such anomalies.

Findings

Significant low-energy deviations found in 3 time-integrated bursts.

Additional spectral components are suggested by deviations in 5 time-resolved bursts.

Method confirms the need for models beyond the standard Band function in some GRBs.

Abstract

A Band function has become the standard spectral function used to describe the prompt emission spectra of gamma-ray bursts (GRBs). However, deviations from this function have previously been observed in GRBs detected by BATSE and in individual GRBs from the \textit{Fermi} era. We present a systematic and rigorous search for spectral deviations from a Band function at low energies in a sample of the first two years of high fluence, long bursts detected by the \textit{Fermi} Gamma-Ray Burst Monitor (GBM). The sample contains 45 bursts with a fluence greater than 2$\times10^{-5}$ erg / cm$^{2}$ (10 - 1000 keV). An extrapolated fit method is used to search for low-energy spectral anomalies, whereby a Band function is fit above a variable low-energy threshold and then the best fit function is extrapolated to lower energy data. Deviations are quantified by examining residuals derived from the extrapolated function and the data and their significance is determined via comprehensive simulations which account for the instrument response. This method was employed for both time-integrated burst spectra and time-resolved bins defined by a signal to noise ratio of 25 $\sigma$ and 50 $\sigma$. Significant deviations are evident in 3 bursts (GRB\,081215A, GRB\,090424 and GRB\,090902B) in the time-integrated sample ($\sim$ 7%) and 5 bursts (GRB\,090323, GRB\,090424, GRB\,090820, GRB\,090902B and GRB\,090926A) in the time-resolved sample ($\sim$ 11%).} The advantage of the systematic, blind search analysis is that it can demonstrate the requirement for an additional spectral component without any prior knowledge of the nature of that extra component. Deviations are found in a large fraction of high fluence GRBs; fainter GRBs may not have sufficient statistics for deviations to be found using this method.