  We present a method to classify initial conditions of a long gamma ray bursts
model sourced by a single relativistic shock. It is based on the use of
artificial neural networks (ANNs) that are trained with light curves (LC)
generated with radiation relativistic hydrodynamics simulations. The model we
use consists in a single shock with a highly relativistic injected beam into a
stratified surrounding medium with profile $1/r^2$. In the process we only
consider the bremsstrahlung radiation and Thomson scattering process. The
initial conditions we use to train the ANN are three: the rest mass density,
Lorentz factor and radiation energy density of the beam that produces the
relativistic shock, together with the LC generated during the process. The
classification selects the location of a box in the 3d parameter space that
better fits a given LC, and in order to decrease the uncertainty of the
parameters this box is refined and the classification selects a new box of
smaller size.

Instrumentation and Methods for Astrophysics;High Energy Astrophysical Phenomena