# Sim-to-Real Domain Adaptation For High Energy Physics

arXiv: https://arxiv.org/abs/1912.08001 · HTML: https://mlsanity.com/paper/1912.08001/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging](https://mlsanity.com/paper/2201.07199/) (2022)
- [AI for Scientific Discovery is a Social Problem](https://mlsanity.com/paper/2509.06580/) (2025)
- [Transfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physics](https://mlsanity.com/paper/2605.07471/) (2026)
- [Safe Domain Adaptation for Physics: Overcoming Nuisances, Label Shifts, and Simulation Priors](https://mlsanity.com/paper/2608.18190/) (2026)

## Abstract (arXiv)

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.