Configurable calorimeter simulation for AI applications
This tool addresses the need for realistic simulation data in high energy physics to aid ML development, but it is incremental as it builds on existing toolkits without demonstrating new breakthroughs.
The authors developed COCOA, a configurable calorimeter simulation tool based on Geant4 and Pythia, to support machine learning algorithms in high energy physics by providing realistic particle shower descriptions for tasks like reconstruction and fast simulation. The tool includes user-configurable geometry and event processing features, but no concrete performance numbers or results are reported.
A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator. This open-source project is aimed to support the development of machine learning algorithms in high energy physics that rely on realistic particle shower descriptions, such as reconstruction, fast simulation, and low-level analysis. Specifications such as the granularity and material of its nearly hermetic geometry are user-configurable. The tool is supplemented with simple event processing including topological clustering, jet algorithms, and a nearest-neighbors graph construction. Formatting is also provided to visualise events using the Phoenix event display software.