Abstract
The jet photosphere has been proposed as the origin for the gamma-ray burst (GRB) prompt emission. In many such models, characteristic features in the spectra appear below the energy range of the Fermi Gamma-ray Burst Monitor (GBM) detectors, so joint fits with X-ray data are important in order to assess the photospheric scenario. Here we consider a particular photospheric model which assumes localized subphotospheric dissipation by internal shocks in a non-magnetized outflow. We investigate it using Bayesian inference and a sample of eight GRBs with known redshifts which are observed simultaneously with Fermi GBM and the Swift X-ray Telescope (XRT). This provides us with an energy range of 0.3 keV-40 MeV and much tighter parameter constraints. We analyze 32 spectra and find that 16 are well described by the model. We also find that the estimates of the bulk Lorentz factor, Γ, and the fireball luminosity, L 0,52, decrease while the fraction of dissipated energy, ϵ d, increases in the joint fits compared to GBM-only fits. These changes are caused by a small excess of counts in the XRT data, relative to the model predictions from fits to GBM-only data. The fact that our limited implementation of the physical scenario yields 50% accepted spectra is promising, and we discuss possible model revisions in the light of the new data. Specifically, we argue that the inclusion of significant magnetization, as well as removing the assumption of internal shocks, will provide better fits at low energies.
Original language | English |
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Article number | 76 |
Journal | Astrophysical Journal |
Volume | 880 |
Issue number | 2 |
DOIs | |
State | Published - 1 Aug 2019 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2019. The American Astronomical Society. All rights reserved..
Funding
Funders | Funder number |
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Not added | |
Horizon 2020 Framework Programme | 773062 |
Science and Technology Facilities Council | ST/N000838/1 |
Keywords
- gamma-ray burst: general
- methods: data analysis
- radiation mechanisms: thermal