Speaker
Description
This project proposes to study a replacement of the solution like the one implemented currently in LHCb, where beam-induced background (BIB) particles are sampled from very large FLUKA output files, with a modern machine learning–based generator. Instead of relying on repeated access to stored datasets, a trained generative model would learn the distributions of BIB particles and produce new samples on demand, conditioned on relevant LHC machine parameters. This would drastically reduce storage requirements and file input/output overhead while maintaining the physical accuracy.
LHCb is currently preparing for the next Run and updating this simulation. The concept of BIB simulation is also not particularly linked to a single experiment, and potential interface between FLUKA BIB and Geant4 signal simulation could be explored in a much wider context.
CERN group/ Experiment
EP-SFT
| Working area | Area 1" Cutting Edge AI for Offline Data Processing |
|---|---|
| Project goals | Generative model for MIB simulation; Integration and validation within at least LHCb; |
| Timeline | Y1 Research of the existing solution(s) Implementation of performance and validations metrics Prototype of a generative model Y2 Implementation in experimental framework (LHCb) Performance study Redesign and retraining |
| Available person power | 0.1 staff |
| Additional person power request | 1 for 2 y (+1y for additional potential experiments) |
| Is this an already ongoing activity? | No |
| Indicative hardware resources needs | Decent GPUs for model development (2-4 local gpus or access to a cluster), shorter-time extended resources for hyperparameter tuning (per each experiment) |