Speaker
Description
Several reconstruction steps in LHC events are being approached using end-to-end deep learning solutions (e.g., for tracking, calorimetry, and particle flow linking). It has been proposed that foundation models trained on physics events could repeat for LHC event reconstruction the astonishing success of Large Language Models in developing multitasking skills. Such an application could be trained on particle flow reconstruction, starting from raw detector inputs to build a global event view without training supervision (unlike the state-of-the-art MLPF model). First efforts towards this Large Physics Models have shown that encouraging results. The use of federated learning could facilitate these efforts: by simultaneously extracting knowledge from various experimental datasets, one could abstract each detector data to a common space, where the underlying physics could be learned more accurately. The knowledge gained with reconstructing CMS data could improve the ATLAS reconstruction, and viceversa. Federated the experience of the CAFEIN team at CERN on large-scale federated learning, we propose to establish an effort to experiment on the idea of cross-experiment learning and assess its potential to improve the physics performance of the various experiments. The success of this process would offer a platform to transfer knowledge from the LHC experiments to future colliders.
CERN group/ Experiment
EP and ATS
| Working area | Area 4: AI Infrastructure for Model Training |
|---|---|
| Project goals | develop a demonstrator of federative learning capabilities for cross-experiment applications |
| Timeline | 3 years |
| Available person power | 0 |
| Additional person power request | 2 PhD |
| Is this an already ongoing activity? | No |
| Indicative hardware resources needs | large cluster of H100/A100 GPUs |