Probing TeV Afterglow Emission of GRB~221009A with Gaussian Structured jet in Wind-driven medium

TL;DR

The work develops a Gaussian structured-jet model for GRB afterglows in a wind-driven medium to explain TeV emission and guide CTA detectability. It integrates jet geometry, wind-modified dynamics, and high-energy radiation processes (SSC with KN effects and EBL attenuation) and validates the framework by fitting GRB 221009A across X-ray, GeV, and TeV bands using MCMC. The analysis reveals that TeV light curves are highly sensitive to the viewing angle, core energy, and wind density, with detectability favored for near-core, high-energy, low-magnetic-field configurations. This approach provides a physically motivated pathway to interpret rare TeV detections and informs observational strategies for CTA in long GRBs.

Abstract

Recent detections of very high energy (VHE; GeV-TeV) photons from gamma-ray burst (GRB) afterglows, most notably the extreme event GRB 221009A, require refined models that include realistic jet structures and complex circumburst environments. The jet's angular structure is crucial for shaping afterglow emission. Our recent work demonstrates that Gaussian jets, with their smooth angular decline, naturally produce early bright peaks for on-axis observers and delayed, softer, dimmer peaks at higher inclinations. The gradual decline suppresses excessive lateral expansion, unlike the sharp edge in top-hat jets, making Gaussian jets a compelling alternative to both top-hat and other structured-jet models. Here we implement a Gaussian structured-jet model to explain TeV afterglows from adiabatic forward shocks propagating in a wind-driven medium. We show that the TeV peak time and flux depend sensitively on jet geometry, kinetic energy, wind density, and on microphysical parameter ratios that scale the SSC component. We identify the afterglow parameter space that is favourable for detecting sub-TeV photons with the Cherenkov Telescope Array (CTA), finding that only about ten per cent of simulated TeV events exceed CTA sensitivity in a wind medium. These detections arise from near core-aligned views, with high kinetic energy and wind density, moderate initial Lorentz factor and downstream magnetic field, and a relatively large fraction of energy in nonthermal electrons. Applying this model to GRB 221009A, we perform multi-band fits including wind-modified dynamics, Klein-Nishina effects, and EBL attenuation, and find that a mildly off-axis geometry reproduces the observed X-ray and GeV-TeV light curves.

Figures (8)

• Figure 1: Time evolution of the SSC afterglow flux at $z=0.151$ is plotted for different jet geometry ($\theta_{v}/\theta_{c}$). We have kept $\theta_{c}$ fixed at $6^{\circ}$ and the curves correspond to varying viewing angles $\theta_v=\{3^\circ,\,6^\circ,\,9^\circ,\,12^\circ,\,15^\circ\}$, corresponds to typical on-axis to off-axis jet scenario. The afterglow parameters are fixed to $E_k=5.5\times10^{52}\,\mathrm{erg}$, $\eta_0=440$, $A_{\star}=0.56$, $\epsilon_e=0.1$, $\epsilon_B=10^{-3}$ and $p=2.5$. All of these SSC fluxes include the EBL correction.
• Figure 2: We plot SSC afterglow flux at $z=0.151$ with varying parameters as in (a) $E_k = \{10^{50}, 10^{52},10^{54}\}\,\mathrm{erg}$, (b) $A_{\star}=\{0.01,\,0.1,\,1.0\}$ and (c) $\eta_c=\{240,\,340,\,540\}$. Otherwise parameters are fixed to $E_k=10^{52}\,\mathrm{erg}$, $\eta_0=440$, $A_{\star}=0.56$, $\epsilon_e=0.1$, $\epsilon_B=10^{-3}$ and $p=2.5$. For the on-axis case (solid curves) $\theta_v=3^\circ$ and for the off-axis case (dashed-dot curves) $\theta_v=9^\circ$, whereas $\theta_{c}$ is kept fixed at $6^{\circ}$. The light-curve plot depicts the SSC flux with EBL correction.
• Figure 3: SSC afterglow light curves at $z=0.151$ for fixed $\epsilon_B=10^{-3}$ and varying $\epsilon_e= \{0.1,\;0.01\}$. Other afterglow parameters are fixed to $E_k=10^{52}\,\mathrm{erg}$, $\eta_0=440$, $\epsilon_e=0.1$, $\epsilon_B=10^{-3}$, and $p=2.5$. We show on-axis, solid curves ($\theta_v=3^\circ$) and off-axis, dashed-dot curves ($\theta_v=9^\circ$) cases kepeping fixed $\theta_c$ at $6^\circ$. Fluxes include attenuation by EBL correction.
• Figure 4: Histograms showing the distribution of all CTA-detected events compared to all simulated samples for parameter $E_k$, $A_{\star}$ and $\eta_c$. These plots depict that higher values of $E_{k}$ and $A_{\star}$ are more probable for CTA detections. Whereas moderately high values of $\eta_c$ favour CTA detectability.
• Figure 5: Histograms illustrate all CTA detected events are more probable at higher values of $\epsilon_{e}$ (left), whereas the nearly uniform distribution in $\epsilon_B$(right) indicates that this parameter has a weaker influence on CTA detectability.