Exploring the viability of physically-motivated models to fit Gamma-ray Burst Spectrum

The detailed nature of the radiative process responsible for gamma-ray burst (GRB) prompt emission has not yet been identified. The prompt emission spectra is often fitted with empirical functions, such as the Band GRB function (BAND), an exponentially attenuated power law (COMP), or a smoothly broken power law (SBPL). The typical slope of the low-energy power law is α ∼ −1 (Gruber et al. 2014; Goldstein et al. 2012). This is higher than the value expected in the case of fast-cooling synchrotron radiation, which predicts that the part of the spectrum immediately below the νFν peak energy should display a power-law behavior with a slope ∼-3/2, which breaks to a harder spectral shape (∼-2/3) at lower energies. Recent works have shown that a number of GRBs have an additional spectral break between ∼1 and a few hundred keV (Oganesyan et al. 2017; Ravasio et al. 2019; Toffano et al. 2021) and the slopes of the power-law below and above this break are consistent with the values expected from synchrotron emission. It has been suggested that the value of α is an average value between the two power-law segments below and above the break energy. In its first 10 years of operation, the Fermi Gamma-ray Burst Monitor (GBM) triggered on 2356 gamma-ray bursts (GRBs), of which 2297 are useful for spectroscopy. This work aims to explore the presence of an additional break in the prompt emission of GRBs, and thus revisit the viability of synchrotron radiation as the source of GRB prompt emission. We pick a sample of ∼500 bright GRBs observed by GBM, based on their energy flux values (Poolakkil et al. 2021). In addition to the usual spectral models used in the GBM spectral catalogs, we also consider two new models; (a) a Band model with a high-energy cutoff, with the power-law indices fixed at the synchrotron values (-0.67 and -1.5 respectively) and the cutoff energy fixed at 33 keV, and (b) a SBPL model with a multiplicative broker power-law (MBPL) added to it, where the break energy of the MBPL is allowed to vary. We then adopt the Bayesian information criterion (BIC-Neath & Cavanaugh (2012)) to compare the fits obtained and choose the best one.