Speaker
Description
In particle scattering experiments, detector effects such as smearing and acceptance, distort the measured data, making it challenging to recover the true underlying physics. In this work, we propose an AI-assisted framework that employs Generative Adversarial Networks (GANs) to mitigate the impact of these distortions, enabling accurate reconstruction of the vertex-level distributions information from detector-level data. We demonstrate our approach using the photoproduction of a single $\pi^0$ as a case study, focusing on the reaction $\gamma p \to p \pi^0$. This reaction, depending on two independent kinematic variables, serves as an ideal test case for our methodology. Our generative model is trained on detector-level pseudodata obtained from a parametrization of the CLAS detector, correcting for smearing and acceptance effects. We apply our GAN-based methodology to perform a closure test using different physics models, comparing synthetic data to experimentally simulated pseudodata. Our results show that the framework effectively reconstructs the full kinematic phase space, including regions of phase space that are not directly measured by the detector, while minimizing model dependence in unmeasured regions. The approach is validated through comparisons of generated and measured event distributions, demonstrating a high level of agreement and the ability to improve the accuracy of physics analyses in particle physics experiments.
