Machine Learning Classification of Sphalerons and Black Holes at the LHC

Abstract

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.Comment: 18 pages, 5 figure