Speakers
Description
The standard Monte Carlo pipeline separates generation, detector simulation and reconstruction. This project advances an end-to-end generative approach that maps truth-level particles directly to reconstructed objects, reducing per-event runtime to ≪ 1 s by bypassing detailed detector simulation and algorithmic reconstruction. For correctly identified objects, the simulation of kinematic properties and efficiencies are essentially a solved problem with existing HEP tools; the emphasis here is on mis-identified particles and fakes that arise from rare combinations of input kinematics and unusual detector interactions. The model is conditioned on pile-up, detector conditions and trigger selections to preserve kinematic correlations. Confusion-aware generation (explicit modelling of mis-ID channels), imbalance-robust training and domain adaptation are used to capture rare effects, while simulation-based inference and uncertainty calibration propagate errors to analysis-level observables. An extensive validation programme benchmarks physics performance against the standard chain across key analyses and systematic scans. The goal is a very fast (≪ 1 s) simulation that remains sufficiently accurate for a wide range of LHC and future-collider studies—particularly searches and systematic-uncertainty evaluations that require large, independent Monte Carlo samples.
CERN group/ Experiment
CERN ATLAS TEam
| Working area | Area 7: Experimental Technologies |
|---|---|
| Project goals | Establish very fast simulation of Monte Carlo events suitable to be used for a sizeable fraction of ATLAS and future detector analyses needs |
| Timeline | Year 1: Investigation of AI model architectures to model each source of mis-identified particles independently Year 2: Integration of these AI models for mis-identified particles into existing approaches to end-to-end simulation and reconstruction Year 3: Validation against ATLAS Geant4 Monte Carlo and tuning to the ATLAS detector simulation and reconstruction |
| Available person power | 0.2 FTE |
| Additional person power request | 36 GRAP months, 36 DOCT monts |
| Is this an already ongoing activity? | No |
| Indicative hardware resources needs | Hardware: current GPU training resources through CERN IT sufficient as long as they stay available at current occupation |