CMS implemented a particle-flow algorithm that reconstructs a complete list of final-state particles per collision, delivering superior performance for jets, hadronic taus, missing transverse momentum, and lepton identification up to 20 pileup interactions.
Arbor, a new approach of the Particle Flow Algorithm
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abstract
The granularity of calorimeter has been revolutionary boosted for future collider experiments. The calorimeter has been pushed to a stage that the sub structure of showers especially hadronic showers can be recorded to a high precision. New reconstruction algorithms are expected from these informations. Following the idea that shower follows the topology of the tree, we developed Arbor, a Particle Flow Algorithm framework. Tested on both simulated data and test beam data, it can successfully separate nearby showers. It has comparable jet energy resolution the best PFA algorithm for International Linear Collider. More importantly, Arbor successfully tags the sub shower structure such as the trajectory of charged particles generated in shower cascade, enabling new approaches for event reconstruction with high granularity calorimeter.
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Deep learning on all particles via holistic analysis and Advanced Color Singlet Identification improves Higgs signal extraction up to sixfold in high-energy collisions.
citing papers explorer
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Particle-flow reconstruction and global event description with the CMS detector
CMS implemented a particle-flow algorithm that reconstructs a complete list of final-state particles per collision, delivering superior performance for jets, hadronic taus, missing transverse momentum, and lepton identification up to 20 pileup interactions.
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Learning from all particles in high-energy collisions
Deep learning on all particles via holistic analysis and Advanced Color Singlet Identification improves Higgs signal extraction up to sixfold in high-energy collisions.