32 citations · 54 across the 6 of their papers we have counts for
10 papers
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
Savannah Thais, Paolo Calafiura, Grigorios Chachamis +7
Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image dat…
Broadening the scope of Education, Career and Open Science in HEP
Sudhir Malik, David DeMuth, Sijbrand de Jong +10
High Energy Particle Physics (HEP) faces challenges over the coming decades with a need to attract young people to the field and STEM careers, as well as a need to recognize, promo…
Symmetry Group Equivariant Architectures for Physics
Alexander Bogatskiy, Sanmay Ganguly, Thomas Kipf +8
Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe. Similarly, in the domain of mac…
Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC
Savannah Thais, Gage DeZoort
3D instance segmentation remains a challenging problem in computer vision. Particle tracking at colliders like the LHC can be conceptualized as an instance segmentation task: begin…
Software Training in HEP
Sudhir Malik, Samuel Meehan, Kilian Lieret +44
Long term sustainability of the high energy physics (HEP) research software ecosystem is essential for the field. With upgrades and new facilities coming online throughout the 2020…
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…