Speaker
Description
We apply machine learning techniques to model the multi-wavelength emission of the extremely bright GRB 210822A using the AFTERGLOWPY library. This approach allows us to estimate the observer angle $\theta_{obs}$, the initial energy $E_0$, the electron index $p$, the thermal energy fractions in electrons ($\epsilon_{e}$) and in the magnetic field ($\epsilon_{B}$), the efficiency $\chi$, and the density of the surrounding medium $n_0$. To achieve this, we train a neural network on 30,000 synthetic AFTERGLOWPY light curves and apply it to this event.
We also analyse the temporal and spectral evolution of the optical and X-ray emissions. Our results show that a reverse shock component dominates the early-time emission, while a jet break is observed at later times. This break allows us to constrain the jet opening angle $\theta_{j}$ to a value consistent with that obtained through the machine learning code.
