IDEA (Innovative Detector for an Electron-positron Accelerator) is an innovative general-purpose detector concept, designed to study electron-positron collisions at future e$^+$e$^-$ circular colliders (FCC-ee and CEPC).
The detector will be equipped with a dual read-out calorimeter able to measure separately the hadronic component and the electromagnetic component of the showers initiated by the impinging hadrons.
Particle flow algorithms (PFAs) have become the paradigm of detector design for the high energy frontier and this talk discusses a project to build a Particle Flow algorithm for the IDEA detector using Machine Learning (ML) techniques. Machine Learning is used for particle reconstruction and identification profiting of the high granularity of the fiber-based dual-readout calorimeter. Neural Networks (NN) are built for electron, pions, neutral kaons, muons reconstruction and identification inside the calorimeter and for the jet reconstruction. The performances of the algorithm using several NN architectures will be shown, with particular attention to the layer setup and the activation function choices. The performances will be evaluated on the resolution function of the reconstructed particles and of the reconstructed jet. The algorithm will be trained using both parallel CPUs and GPU, and the time performances and the memory usage of the two approaches will be systematically compared.
Finally, the aim of the project is to develop the NN algorithm inside the Pandora PFA framework.
|Consider for long presentation||Yes|