# UR: Making the Most of Radio Peaks in Gamma Ray Burst Afterglows

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Ruby Duncan

George Washington University

This guest post was written by Ruby Duncan. Ruby is an undergraduate senior pursuing a degree in Astronomy and Astrophysics at George Washington University. She’s been working on research projects focusing on gamma-ray burst afterglows since the summer of 2018, though this work will be her capstone research project before graduating.

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

There are several physical parameters that can inform 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 burst, and the energy in the associated radiation-producing electrons.  These parameters can be constrained by modeling the energy emitted by the GRB across the entire electromagnetic spectrum. This modeling is typically done using multiwavelength observations, but those observations are not always available. My work focuses on determining GRB parameters from restricted parts of the spectrum so that a robust picture of bursts can be developed even from limited observations.

The afterglow photons that are seen at radio wavelengths are especially useful. Peaks seen in the light curves from radio afterglow 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 observed energy, as well as the time and flux of the peak seen in the radio light curve. We have expanded upon their work by including peaks of radio spectra (frequency versus flux) along with radio light curves (flux versus time) and also by adding 15 more GRBs to the initial sample of 36. This allowed for a systematic check of the modeling approach, as well as enabling us to further constrain the distribution of $\epsilon_{e}$. We further expanded this work by exploring other parameters of particle acceleration that could potentially be constrained using our determined values of electron energy ($\epsilon_{e}$).

Astrobite edited by: Tarini Konchady