Statistical properties of Fermi GBM GRBs' spectra
Abstract
Statistical studies of gammaray burst (GRB) spectra may result in important information on the physics of GRBs. The Fermi GBM catalogue contains GRB parameters (peak energy, spectral indices, and intensity) estimated fitting the gammaray spectral energy distribution of the total emission (fluence, flnc), and during the time of the peak flux (pflx). Using contingency tables, we studied the relationship of the models bestfitting pflx and flnc time intervals. Our analysis revealed an ordering of the spectra into a power law  Comptonized  smoothly broken power law  Band series. This result was further supported by a correspondence analysis of the pflx and flnc spectra categorical variables. We performed a linear discriminant analysis (LDA) to find a relationship between categorical (spectral) and model independent physical data. LDA resulted in highly significant physical differences among the spectral types, that is more pronounced in the case of the pflx spectra, than for the flnc spectra. We interpreted this difference as caused by the temporal variation of the spectrum during the outburst. This spectral variability is confirmed by the differences in the lowenergy spectral index and peak energy, between the pflx and flnc spectra. We found that the synchrotron radiation is significant in GBM spectra. The mean lowenergy spectral index is close to the canonical value of α = 2/3 during the peak flux. However, α is ∼ 0.9 for the spectra of the fluences. We interpret this difference as showing that the effect of cooling is important only for the fluence spectra.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 March 2018
 DOI:
 10.1093/mnras/stx3152
 arXiv:
 arXiv:1712.00389
 Bibcode:
 2018MNRAS.475..306R
 Keywords:

 methods: data analysis;
 methods: statistical;
 gammaray burst: general;
 cosmology: miscellaneous;
 gammarays: general;
 Astrophysics  High Energy Astrophysical Phenomena
 EPrint:
 Manuscript accepted for publication in MNRAS