activity
20192024
most citedSpeeding up Particle Track Reconstruction using a Parallel Kalman Filter Algorithm

15 citations · 19 across the 5 of their papers we have counts for

collaborators

5 papers

hep-ex2024

Line Segment Tracking: Improving the Phase 2 CMS High Level Trigger Tracking with a Novel, Hardware-Agnostic Pattern Recognition Algorithm

Emmanouil Vourliotis, Philip Chang, Peter Elmer +11

Charged particle reconstruction is one the most computationally heavy components of the full event reconstruction of Large Hadron Collider (LHC) experiments. Looking to the future,…

physics.ins-det2022

Segment Linking: A Highly Parallelizable Track Reconstruction Algorithm for HL-LHC

Philip Chang, Peter Elmer, Yanxi Gu +9

The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) will produce particle collisions with up to 200 simultaneous proton-proton interactions. These unprecedented condi…

physics.ins-det2020★ 15 cited

Speeding up Particle Track Reconstruction using a Parallel Kalman Filter Algorithm

Steven Lantz, Kevin McDermott, Michael Reid +16

One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is determining the trajectory of charged particles during event…

physics.ins-det2020★ 2 cited

Reconstruction of Charged Particle Tracks in Realistic Detector Geometry Using a Vectorized and Parallelized Kalman Filter Algorithm

Giuseppe Cerati, Peter Elmer, Brian Gravelle +14

One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is finding and fitting particle tracks during event reconstruct…

physics.ins-det2019★ 2 cited

Speeding up Particle Track Reconstruction in the CMS Detector using a Vectorized and Parallelized Kalman Filter Algorithm

Giuseppe Cerati, Peter Elmer, Brian Gravelle +13

Building particle tracks is the most computationally intense step of event reconstruction at the LHC. With the increased instantaneous luminosity and associated increase in pileup…