Speaker
Description
For the CLAS Collaboration
As part of the efforts to gain more insights into the bound nucleon structure and the associated in-medium modifications that led to the still-to-be-unraveled EMC (European Muon Collaboration) effect, novel approaches can be deployed using, for example, the deeply virtual Compton scattering (DVCS) process to probe the partonic structure of light nuclei, such as 2H and 4He, and thus, study the related in-medium stimulated effects and their impact on the correlated three-dimensional Generalized Parton Distributions. The planned 2025 CLAS12 experiment aims to use the newly built a low energy recoil tracker (ALERT) detector to study tagged DVCS on 4He with an 11 GeV beam energy via the detection of low-momentum recoil fragments such as 2H, 3H, 3He, 4He, and protons, down to 70 MeV/c, in a wide kinematical range. The ALERT detector enables effectual separation between various recoil ions by integrating a hyperbolic drift chamber (AHDC) with a time-of-flight (ATOF) array.
Recent advances in artificial intelligence (AI), such as new model architectures, have proven effective for high-rate experiments with substantially elevated background noise, such as AHDC in ALERT. AI is deployed in ALERT experiments to enhance AHDC track-finding efficiency, purity, and speed compared to conventional algorithms as well as particle identification in conjunction with ATOF. In this talk, an overview of the ALERT physics program will be provided alongside the ongoing development and optimization of the AI-assisted track reconstruction and particle identification techniques.
This work is supported in part by the U.S. DOE award #: DE-FG02-07ER41528.
