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Found 9 papers, 3 papers with code
Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors
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 Circular Collider.
Progress towards an improved particle flow algorithm at CMS with machine learning
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 CERN LHC, and has been a focus of development in light of planned Phase-2 running conditions with an increased pileup and detector granularity.
Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning
The abundance of dark matter (DM) subhalos orbiting a host galaxy is a generic prediction of the cosmological framework, and is a promising way to constrain the nature of DM.
Hyperparameter optimization of data-driven AI models on HPC systems
This work exercises High Performance Computing resources to perform large-scale hyperparameter optimization using distributed training on multiple compute nodes.
Machine Learning for Particle Flow Reconstruction at CMS
The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruction that exploits the combined information of multiple detector subsystems, leading to strong improvements for quantities such as jets and missing transverse energy.
Explaining machine-learned particle-flow reconstruction
The particle-flow (PF) algorithm is used in general-purpose particle detectors to reconstruct a comprehensive particle-level view of the collision by combining information from different subdetectors.
MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum.
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision.
Instrumentation and Detectors High Energy Physics - Experiment Computational Physics Data Analysis, Statistics and Probability
Processing Columnar Collider Data with GPU-Accelerated Kernels
At high energy physics experiments, processing billions of records of structured numerical data from collider events to a few statistical summaries is a common task.
Data Analysis, Statistics and Probability Distributed, Parallel, and Cluster Computing Computational Physics
