Unfolding Particle Detector Acceptance in High Energy Physics with Generative AI

The “acceptance problem” in high energy physics (HEP) refers to the challenge of accurately modeling detector acceptance to ensure the precision of measurements. This study explores the application of Generative AI to address the acceptance problem in HEP. By training a Generative Adversarial Network (GAN) on simulated detector data (pseudo-data), we demonstrate its capability to learn detector responses and generate synthetic data that closely match measured distributions. A key component of our methodology is a custom generator loss function that incorporates physics-informed principles to improve training. This custom loss function penalizes deviations from the true distribution of event components, ensuring that the generated samples adhere to the underlying physics. Additionally, we trained a binary classifier to distinguish between different topological states (measured and unmeasured events) within the generated Monte Carlo pseudodata, further refining the model's accuracy. Our approach preserves correlations between kinematic variables across multiple dimensions, providing an accurate representation of the underlying physics. Validation with Monte Carlo pseudodata demonstrates the method's ability to recover true distributions even in regions with limited detector sensitivity, establishing a solid foundation for applying our framework to real experimental data. Our results highlight the feasibility and advantages of using generative AI in HEP, paving the way for broader applications in the field.