Machine-learning based particle-flow algorithm in CMS
By: Farouk Mokhtar
Potential Business Impact:
Helps scientists see tiny particles better.
The particle-flow (PF) algorithm provides a global event description by reconstructing final-state particles and is central to event reconstruction in CMS. Recently, end-to-end machine learning (ML) approaches have been proposed to directly optimize physical quantities of interest and to leverage heterogeneous computing architectures. One such approach, machine-learned particle flow (MLPF), uses a transformer model to infer particles directly from tracks and clusters in a single pass. We present recent CMS developments in MLPF, including training datasets, model architecture, reconstruction metrics, and integration with offline reconstruction software.
Similar Papers
Multimodal Generative Flows for LHC Jets
High Energy Physics - Phenomenology
Makes computer simulations of particle collisions better.
Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders
High Energy Physics - Experiment
Teaches computers to learn from different science experiments.
Test Time Training for AC Power Flow Surrogates via Physics and Operational Constraint Refinement
Machine Learning (CS)
Makes power grids predict electricity flow accurately.