Speaker
Description
End-to-End Fast Simulation of the ALICE Zero Degree Calorimeter using Generative Models
Davide Fuligno
On behalf of the ALICE Collaboration
Università di Pisa and INFN, Trieste Italy
The ALICE experiment at the LHC faces unprecedented computing challenges in Run 3 and 4, necessitating innovative solutions to cope with the increased data-taking luminosity and the continuous readout. A critical bottleneck in the current simulation pipeline for Pb-Pb collisions is the Zero Degree Calorimeter (ZDC), which characterizes collision geometry by detecting spectator nucleons. The full Geant4 transport simulation of hadronic showers in the ZDC is currently so computationally expensive that it is omitted from standard Monte Carlo productions, resulting in the absence of a realistic modeling of forward energy, centrality, and spectator-nucleon multiplicity.
In this contribution, we present a novel deep-learning-based fast simulation framework designed to overcome this limitation. We employ a generative architecture combining an encoder with a neural network, trained on simulated samples of spectator protons and neutrons in Pb-Pb collisions. A distinguishing feature of our approach is its end-to-end capability. The model bypasses traditional hit generation and digitization steps by directly predicting the detector response in the form of digitized outputs. This allows the direct reconstruction of structures compatible with the ALICE Analysis Object Data format, thereby streamlining the entire simulation chain.
The inference engine is designed for seamless integration into the ALICE O2 software framework using the ONNX standard, ensuring portability across heterogeneous computing resources. Preliminary results indicate a computational speed-up of approximately six orders of magnitude compared to the full simulation.We will report on architecture optimization, hyperparameter tuning, and the comparative evaluation of generative models, including Normalizing Flows, Diffusion Models, and Conditional Flow Matching. These results, supported by validation studies, demonstrate the potential to enable high-statistics ZDC simulation in future ALICE production campaigns.