Speakers
Description
Detailed event simulation at the LHC is taking a large fraction of computing budget. CMS developed an end-to-end ML based simulation that can speed up the time for production of analysis samples of several orders of magnitude with a limited loss of accuracy. As the CMS experiment is adopting a common analysis level format, the NANOAOD, for a larger number of analyses, such an event representation is used as the target of this ultra fast simulation that we call FlashSim. Generator level events, from PYTHIA or other generators, are directly translated into NANOAOD events at several hundred Hz rate with FlashSim. We show how training FlashSim on a limited number of full simulation events is sufficient to achieve very good accuracy on larger datasets for processes not seen at training time. Comparisons with full simulation samples in some simplified benchmark analysis are also shown. With this work, we aim at establishing a new paradigm for LHC collision simulation workflows, for offline and scouting datasets, in view of HL-LHC.
CERN group/ Experiment
EP-CMG
| Working area | Area 1" Cutting Edge AI for Offline Data Processing |
|---|---|
| Project goals | support the development of new ML algorithms and the integration of FlashSim in the CMS collaboration |
| Timeline | at least 3 years |
| Available person power | two users (PhD students) |
| Additional person power request | 1 fellow or a PhD student |
| Is this an already ongoing activity? | Yes |
| Indicative hardware resources needs | Access to a GPU cluster with LCG-like software stack and cvmfs access with fast storage facilities across the full duration of the project |