19 citations · 41 across the 5 of their papers we have counts for
5 papers · 1 filter
Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors
Joosep Pata, Eric Wulff, Farouk Mokhtar +4
Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Ci…
Progress towards an improved particle flow algorithm at CMS with machine learning
Farouk Mokhtar, Joosep Pata, Javier Duarte +3
The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at the CER…
Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC
Eric Wulff, Maria Girone, David Southwick +2
Training and Hyperparameter Optimization (HPO) of deep learning-based AI models are often compute resource intensive and calls for the use of large-scale distributed resources as w…
Hyperparameter optimization of data-driven AI models on HPC systems
Eric Wulff, Maria Girone, Joosep Pata
In the European Center of Excellence in Exascale computing "Research on AI- and Simulation-Based Engineering at Exascale" (CoE RAISE), researchers develop novel, scalable AI techno…
Machine Learning for Particle Flow Reconstruction at CMS
Joosep Pata, Javier Duarte, Farouk Mokhtar +5
We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based on ca…