paper

Photospheric Prompt Emission From Long Gamma Ray Burst Simulations -- I. Optical Emission

arXiv:2105.06505 · doi:10.3847/1538-4357/ac2428

Abstract

A complete understanding of Gamma Ray Bursts (GRBs) has been difficult to achieve due to our incomplete knowledge of the radiation mechanism that is responsible for producing the prompt emission. This emission, which is detected in the first tens of seconds of the GRB, is typically dominated by hard X-ray and gamma ray photons although, there have also been a few dozen prompt optical detections. These optical detections have the potential to discriminate between plausible prompt emission models, such as the photospheric and synchrotron shock models. In this work we use an improved MCRaT code, which includes cyclo-synchrotron emission and absorption, to conduct radiative transfer calculations from optical to gamma ray energies under the photospheric model. The calculations are conducted using a set of two dimensional relativistic hydrodynamic long GRB jet simulations, consisting of a constant and variable jet. We predict the correlations between the optical and gamma ray light curves as functions of observer angle and jet variability and find that there should be extremely dim optical prompt precursors for large viewing angles. Additionally, the detected optical emission originates from dense regions of the outflow such as shock interfaces and the jet-cocoon interface. Our results also show that the photospheric model is not able to account for the current set of optical prompt detections that have been made and additional radiative mechanisms are needed to explain these prompt optical observations. These findings show the importance of conducting global radiative transfer simulations using hydrodynamically calculated jet structures.

arXiv comment22 pages, with 5 pages for the appendix; 11 figures; accepted to ApJ; the MCRaT code is open source and available at: https://github.com/lazzati-astro/MCRaT and the ProcessMCRaT code is also open source and available at: https://github.com/parsotat/ProcessMCRaT

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