Sim-to-Real Domain Adaptation For High Energy Physics

TL;DR

This paper applies domain adaptation with a Domain Adversarial Neural Network to improve the transfer of machine learning models from simulated to real data in high energy physics, addressing discrepancies between simulation and reality.

Contribution

It introduces a novel application of domain adversarial training for sim-to-real transfer in high energy physics, enhancing ML model robustness across datasets.

Findings

Successful domain adaptation achieved with neural networks

Improved consistency of ML performance on real and simulated data

Demonstrated approach on public HEP datasets

Abstract

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.