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# UR: making the most of radio peaks in gamma-ray afterglow

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Ruby duncan

George Washington University

This guest post was written by Ruby duncan. Ruby is an undergraduate student pursuing a degree in Astronomy and Astrophysics at George Washington University. She has been working on research projects focusing on the afterglow of gamma-ray bursts since the summer of 2018, although this work is her flagship research project before graduating.

Gamma-ray bursts (GRBs) are caused by extremely energetic jets resulting from cataclysmic events, which can produce as much energy in seconds as our Sun will throughout its lifetime (10 billion years!). These jets emit short bursts of gamma rays, followed by afterglow of photons that range from x-rays to radio waves. Observations of GRBs and their afterglow across the electromagnetic spectrum can be used to study particle acceleration under extreme conditions.

There are several physical parameters that can tell us about a burst and its environment. These include the energy of the shock wave at the front of the jet, the density of the medium surrounding the explosion and the energy in the associated jet. electrons producing radiation. These parameters can be constrained by modeling the energy emitted by the GRB over the entire electromagnetic spectrum. This modeling is usually done using observations at multiple wavelengths, but these observations are not always available. My work focuses on determining GRB parameters from restricted parts of the spectrum so that a robust image of bursts can be developed even from limited observations.

The afterglow photons which are seen at radio wavelengths are particularly useful. The peaks observed in the light curves from radio remanence emission can be used to constrain the fraction of the shock energy that resides in electrons, $epsilon_ {e}$. This diagnostic tool, developed by Beniamini & van der Horst (2017), depends on a relationship between the observables of a GRB, such as its redshift and the observed energy, as well as the time and flux of the peak seen in the radio light curve. We expanded their work by including peaks of radio spectra (frequency versus flux) as well as radio light curves (flux versus time) and also adding 15 more GRBs to the initial sample of 36. This has allowed a systematic verification of the modeling. approach, while allowing us to further restrict the distribution of $epsilon_ {e}$. We deepened this work by exploring other particle acceleration parameters that could potentially be constrained using our determined values ​​of electron energy ($epsilon_ {e}$).