  A novel method for identifying the nature of QCD transitions in heavy-ion
collision experiments is introduced. PointNet based Deep Learning (DL) models
are developed to classify the equation of state (EoS) that drives the
hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV.
The DL models were trained and evaluated in different hypothetical experimental
situations. A decreased performance is observed when more realistic
experimental effects (acceptance cuts and decreased resolutions) are taken into
account. It is shown that the performance can be improved by combining multiple
events to make predictions. The PointNet based models trained on the
reconstructed tracks of charged particles from the CBM detector simulation
discriminate a crossover transition from a first order phase transition with an
accuracy of up to 99.8%. The models were subjected to several tests to evaluate
the dependence of its performance on the centrality of the collisions and
physical parameters of fluid dynamic simulations. The models are shown to work
in a broad range of centralities (b=0-7 fm). However, the performance is found
to improve for central collisions (b=0-3 fm). There is a drop in the
performance when the model parameters lead to reduced duration of the fluid
dynamic evolution or when less fraction of the medium undergoes the transition.
These effects are due to the limitations of the underlying physics and the DL
models are shown to be superior in its discrimination performance in comparison
to conventional mean observables.
