  The deep learning technique has been applied for the first time to
investigate the possibility of centrality determination in terms of the number
of participants ($N_{\mathrm{part}}$) in high-energy heavy-ion collisions. For
this purpose, supervised learning using both deep neural network (DNN) and
convolutional neural network (CNN) is performed with labeled data obtained by
modeling relativistic heavy-ion collisions utilizing A Multi-phase Transport
Model (AMPT). Event-by-event distributions of pseudorapidity and azimuthal
angle of charged hadrons weighted by their transverse momentum are used as
input to train the DL models. The DL models did remarkably well in predicting
$N_{\mathrm{part}}$ values with CNN slightly outperforming the DNN model. The
Mean Squared Logarithmic Error (MSLE) for the CNN model (Model-4) is determined
to be 0.0592 for minimum bias collisions and 0.0114 for 0-60\% centrality
class, indicating that the model performs better for semi-central and central
collisions. Furthermore, the studied DL model is proven to be robust to changes
in energy as well as model parameters of the input. The current study
demonstrates that the data-driven technique has a distinct potential for
determining centrality in terms of the number of participants in high-energy
heavy-ion collision experiments.
