Sim-to-Real Domain Adaptation For High Energy Physics
This addresses the critical issue of simulation-reality discrepancies in high energy physics analysis, which can lead to unreliable results, but the approach is incremental as it applies an existing domain adaptation method to this specific domain.
The paper tackles the problem of performance degradation in machine learning algorithms for high energy physics when trained on simulated data and applied to real experimental data, by applying a Domain Adversarial Neural Network to achieve successful sim-to-real transfer and ensure consistent performance across datasets.
Particle physics or High Energy Physics (HEP) studies the elementary constituents of matter and their interactions with each other. Machine Learning (ML) has played an important role in HEP analysis and has proven extremely successful in this area. Usually, the ML algorithms are trained on numerical simulations of the experimental setup and then applied to the real experimental data. However, any discrepancy between the simulation and real data may lead to dramatic consequences concerning the performances of the algorithm on real data. In this paper, we present an application of domain adaptation using a Domain Adversarial Neural Network trained on public HEP data. We demonstrate the success of this approach to achieve sim-to-real transfer and ensure the consistency of the ML algorithms performances on real and simulated HEP datasets.