  In models with large extra dimensions, "miniature" black holes (BHs) might be
produced in high-energy proton-proton collisions at the Large Hadron Collider
(LHC). In the semi-classical regime, those BHs thermally decay, giving rise to
large-multiplicity final states with jets and leptons. On the other hand,
similar final states are also expected in the production of electroweak
sphaleron/instanton-induced processes. We investigate whether one can
discriminate these scenarios when BH or sphaleron-like events are observed in
the LHC using Machine Learning (ML) methods. Classification among several BH
scenarios with different numbers of extra dimensions and the minimal BH masses
is also examined. In this study we consider three ML models: XGBoost algorithms
with (1) high- and (2) low-level inputs, and (3) a Residual Convolutional
Neural Network. In the latter case, the low-level detector information is
converted into an input format of three-layer binned event images, where the
value of each bin corresponds to the energy deposited in various detector
subsystems. We demonstrate that only a few detected events are sufficient to
effectively discriminate between the sphaleron and BH processes. Separation
among BH scenarios with different minimal BH masses is also possible with a
reasonable number of events, that can be collected in the LHC Run-2, -3 and the
high-luminosity LHC (HL-LHC). We find, however, that a large number of events
is needed to discriminate between BH hypotheses with the same minimal BH mass,
but different numbers of extra dimensions.

High Energy Physics - Phenomenology;High Energy Physics - Experiment