GRAIL

The project aims to revolutionize data analysis in particle physics by integrating advanced Deep Learning techniques with the computational capabilities of NVIDIA CUDA GPUs, with a specific focus on data from calorimeters. This approach seeks to overcome the limitations of conventional methodologies, significantly improving energy measurement precision and event reconstruction capabilities.
The activities are structured into two main phases: the study and development of innovative algorithms and their adaptation for optimized execution on GPUs, followed by a rigorous process of testing and optimization. The scientific methodologies employed range from particle physics to artificial intelligence, combining detailed analysis of experimental data with cutting-edge deep learning techniques.

The objectives include optimizing energy resolution, advancing event reconstruction, and increasing computational efficiency, ultimately pushing the boundaries of particle physics research. The project aligns with the research program’s priorities by emphasizing innovation, methodological consistency, and economic sustainability, with particular attention to leveraging advanced computational resources to handle large data volumes.

The introduction of neural networks, such as CNNs, GNNs, or RNNs, in the context of calorimeters represents a significant innovation, with potential implications both in the scientific domain and in practical applications. Furthermore, the efficient use of NVIDIA CUDA GPUs ensures the project’s economic sustainability by reducing operational costs associated with data analysis.