  Machine learning algorithms have the capacity to discern intricate features
directly from raw data. We demonstrated the performance of top taggers built
upon three machine learning architectures: a BDT that uses jet-level variables
(high-level features, HLF) as input, while a CNN trained on the jet image, and
a GNN trained on the particle cloud representation of a jet utilizing the
4-momentum (low-level features, LLF) of the jet constituents as input. We found
significant performance enhancement for all three classes of classifiers when
trained on combined data from calorimeter towers and tracker detectors. The
high resolution of the tracking data not only improved the classifier
performance in the high transverse momentum region, but the information about
the distribution and composition of charged and neutral constituents of the fat
jets and subjets helped identify the quark/gluon origin of sub-jets and hence
enhances top tagging efficiency. The LLF-based classifiers, such as CNN and
GNN, exhibit significantly better performance when compared to HLF-based
classifiers like BDT, especially in the high transverse momentum region.
Nevertheless, the LLF-based classifiers trained on constituents' 4-momentum
data exhibit substantial dependency on the jet modeling within Monte Carlo
generators. The composite classifiers, formed by stacking a BDT on top of a
GNN/CNN, not only enhance the performance of LLF-based classifiers but also
mitigate the uncertainties stemming from the showering and hadronization model
of the event generator. We have conducted a comprehensive study on the
influence of the fat jet's reconstruction and labeling procedure on the
efficiency of the classifiers. We have shown the variation of the classifier's
performance with the transverse momentum of the fat jet.
