activity
20162025
most citedGraph Neural Networks for Particle Reconstruction in High Energy Physics detectors

86 citations · 333 across the 24 of their papers we have counts for

collaborators
Showing 2020Show all

8 papers · 1 filter

physics.ins-det2020★ 7 cited

The Analog Front-end for the LGAD Based Precision Timing Application in CMS ETL

Quan Sun, Sunil M. Dogra, Christopher Edwards +20

The analog front-end for the Low Gain Avalanche Detector (LGAD) based precision timing application in the CMS Endcap Timing Layer (ETL) has been prototyped in a 65 nm CMOS mini-ASI…

physics.ins-det2020★ 32 cited

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs

Aneesh Heintz, Vesal Razavimaleki, Javier Duarte +18

We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a fram…

hep-ph2020

Analysis Description Languages for the LHC

Sezen Sekmen, Philippe Gras, Lindsey Gray +6

An analysis description language is a domain specific language capable of describing the contents of an LHC analysis in a standard and unambiguous way, independent of any computing…

physics.comp-ph2020

HL-LHC Computing Review: Common Tools and Community Software

HEP Software Foundation, :, Thea Aarrestad +107

Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will b…

cs.DC2020★ 22 cited

Coffea -- Columnar Object Framework For Effective Analysis

Nicholas Smith, Lindsey Gray, Matteo Cremonesi +8

The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility…

physics.ins-det2020★ 12 cited

Track Seeding and Labelling with Embedded-space Graph Neural Networks

Nicholas Choma, Daniel Murnane, Xiangyang Ju +16

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promisi…