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Particle physics or High Energy Physics (HEP) studies the elementaryconstituents of matter and their interactions with each other. Machine Learning(ML) has played an important role in HEP analysis and has proven extremelysuccessful in this area. Usually, the ML algorithms are trained on numericalsimulations of the experimental setup and then applied to the real experimentaldata. However, any discrepancy between the simulation and real data may lead todramatic consequences concerning the performances of the algorithm on realdata. In this paper, we present an application of domain adaptation using aDomain Adversarial Neural Network trained on public HEP data. We demonstratethe success of this approach to achieve sim-to-real transfer and ensure theconsistency of the ML algorithms performances on real and simulated HEPdatasets.

Sim-to-Real Domain Adaptation For High Energy Physics