# A Reverse Shock in GRB 181201A

## TL;DR

This paper reports multiwavelength observations of GRB 181201A, revealing a reverse shock component, and uses detailed modeling to derive physical parameters, highlighting the importance of low-density environments for reverse shock detection.

## Contribution

First joint ALMA-VLA-GMRT observations of a GRB afterglow enabling direct decomposition of reverse and forward shock emissions, with comprehensive modeling of physical parameters.

## Key findings

- Detection of reverse shock emission at 3.9 days.
- Derived initial Lorentz factor of approximately 103.
- Low magnetization and mildly relativistic reverse shock.

## Abstract

We present comprehensive multiwavelength radio to X-ray observations of GRB 181201A spanning from $\approx150$ s to $\approx163$ days after the burst, comprising the first joint ALMA-VLA-GMRT observations of a gamma-ray burst (GRB) afterglow. The radio and mm-band data reveal a distinct signature at $\approx3.9$ days, which we interpret as reverse shock (RS) emission. Our observations present the first time that a single radio-frequency spectral energy distribution can be decomposed directly into RS and forward shock (FS) components. We perform detailed modeling of the full multiwavelength data set, using Markov Chain Monte Carlo sampling to construct the joint posterior density function of the underlying physical parameters describing the RS and FS synchrotron emission. We uncover and account for all degeneracies in the model parameters. The joint RS-FS modeling reveals a weakly magnetized ($\sigma\approx3\times10^{-3}$), mildly relativistic RS, from which we derive an initial bulk Lorentz factor of $\Gamma_0\approx103$ for the GRB jet. Our results support the hypothesis that low-density environments are conducive to the observability of RS emission. We compare our observations to other events with strong RS detections, and find a likely observational bias selecting for longer lasting, non-relativistic reverse shocks. We present and begin to address new challenges in modeling posed by the present generation of comprehensive, multi-frequency data sets.