PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 2, 2026Monthly Notices of the Royal Astronomical Society0 citationsOpen Access

Systematic Error in Approximate Models of the GRB Early Afterglow

View Full Paper
BABenjamin AmendECEric R. CoughlinJZJonathan Zrake

Key Points

Key points are not available for this paper at this time.

Abstract

Abstract Gamma-ray burst (GRB) afterglows are thought to arise when relativistic ejecta launched by a compact central engine drive a blast wave into the surrounding circumburst medium, producing broadband synchrotron emission. We present a rigorous assessment, based on high-resolution special relativistic hydrodynamics simulations, of a widely adopted ‘two-zone model’ for approximating the dynamics of the early afterglow phase. Before the onset of the Blandford-McKee (BMK) self-similar solution, the outflow generally produces two emission components, associated with the forward-shocked circumburst medium and the reverse-shocked ejecta. The subsequent evolution depends on whether the reverse shock significantly decelerates the ejecta as it crosses the shell, separating the so-called relativistic and Newtonian reverse shock regimes. We show that when the reverse shock is Newtonian, it crosses the ejecta shell long before BMK self-similarity is established, leaving a prolonged interval that can span ~ hours in observer time in which the true hydrodynamic evolution is not captured by standard semi-analytic prescriptions. We demonstrate that this mismatch can, for representative afterglow parameters, substantially overpredict the reverse-shock emission from radio through ultraviolet frequencies, or overpredict the forward-shock emission at X-ray frequencies, depending on how the transition away from the two-zone model is prescribed.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Amend et al. (2026) studied this question.

synapsesocial.com/papers/6a97e20ec562ede874ec622chttps://doi.org/10.1093/mnras/stag1611
Ask AI
Helpful
Bookmark
Share
View Full Paper