most citedSearch for new physics in top quark production with additional leptons in proton-proton collisions at 13 TeV using effective field theory

32 citations · 72 across the 3 of their papers we have counts for

collaborators

7 papers

cs.LG20218 cited

hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Farah Fahim, Benjamin Hawks, Christian Herwig +27

Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains.…

hep-ex202032 cited

Search for new physics in top quark production with additional leptons in proton-proton collisions at 13 TeV using effective field theory

CMS Collaboration

Events containing one or more top quarks produced with additional prompt leptons are used to search for new physics within the framework of an effective field theory (EFT). The dat…

physics.ins-det202032 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…

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…

physics.ins-det2020

Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics

Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta +19

Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering…

cs.LG2020

Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML

Giuseppe Di Guglielmo, Javier Duarte, Philip Harris +13

We present the implementation of binary and ternary neural networks in the hls4ml library, designed to automatically convert deep neural network models to digital circuits with FPG…