Sim-to-Real Domain Adaptation For High Energy Physics

Marouen Baalouch (1) , Jean-Philippe Poli (1) , Maxime Defurne (2) , Noëlie Cherrier (2)

1 LI3A (CEA, LIST) - Intelligence Artificielle et Apprentissage Automatique (CEA, LIST)
2 IRFU - Institut de Recherches sur les lois Fondamentales de l'Univers

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.

Mots clés: machine learning; artificial intelligence; online learning; High Energy Physics; simulation; Domain Adversarial Neural Network; neural network

Domaines: Intelligence artificielle [cs.AI]; Physique des Hautes Energies - Phénoménologie [hep-ph]; Apprentissage [cs.LG]; Réseau de neurones [cs.NE]

Marouen Baalouch, Jean-Philippe Poli, Maxime Defurne, Noëlie Cherrier. Sim-to-Real Domain Adaptation For High Energy Physics. 33rd Annual Conference on Neural Information Processing Systems, Dec 2019, Vancouver, Canada. ⟨hal-03644918⟩