Numerical investigations of the progenitors of radio loud, long gamma-ray bursts

ORAL

Abstract

We investigate a progenitor model for radio loud, long gamma-ray bursts (lGRB) where a massive star collapses in an interacting binary system with a stellar black hole companion. Using multi-code, numerical simulations, we make predictions for both the prompt and afterglow emission from these systems, through a detailed study of the jet development and its propagation into the surrounding medium. As these systems eventually evolve into binary black hole systems, our results allow us to put constraints on formation channels of gravitational wave events potentially detectable by LIGO/Virgo. In this talk, we focus on one aspect of our approach to this multi-physics, multi-scale problem in using the general relativistic hydrodynamics code Athena++ to model the post-collapse, black hole and disk central engine that leads to a lGRB. Our initial conditions of black hole spin and accretion disk mass are given by a set of numerical simulations of the binary system, prior to collapse, using Modules for Experiments in Stellar Astrophysics (MESA). In a parameter space investigation, we simulate the jet duration to place constraints on the radio afterglow emission spectra and light curves.

*This work is supported by the US Department of Energy through the Los Alamos National Laboratory. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract No. 89233218CNA000001). LA-UR-23-20090. Research is supported by the Laboratory Directed Research and Development program of Los Alamos National Laboratory under project number 20230115ER. This research uses resources provided by the Los Alamos National Laboratory Institutional Computing Program.

Presenters

  • Celia Toral

    • Cornell University

Authors

  • Celia Toral

    • Cornell University
  • Roseanne M Cheng

    • Los Alamos National Laboratory
  • Nicole M Lloyd-Ronning

    • Los Alamos National Lab
  • Ken Luu

    • San Francisco State University
  • Lailani Kenoly

    • De Anza College