Speaker
Alexander Joseph Schuy
(University of Washington (US))
Description
The search for dark matter and other new physics at the Large Hadron Collider (LHC) involves enormous data collection. Due to this, a high-level trigger system (HLT) must decide which data to keep for long-term storage while maintaining high throughput and on the order of millisecond latency. A central part of the HLT is 3D clustering of low-level detector measurements in the calorimeter. In this work, we show low-latency, high-throughput 3D calorimetry clustering using Sparse Point-Voxel Convolutional Neural Networks (SPVCNN) that can be deployed at-scale to heterogeneous computing systems while maintaining or exceeding the performance of conventional algorithms.
Primary authors
Alexander Joseph Schuy
(University of Washington (US))
Jeffrey Krupa
(Massachusetts Institute of Technology)
Philip Coleman Harris
(Massachusetts Inst. of Technology (US))
Scott Hauck
Shih-Chieh Hsu
(University of Washington Seattle (US))
Song Han
(Massachusetts Institute of Technology)
William Patrick Mccormack
(Massachusetts Inst. of Technology (US))
Zhijian Liu
(Massachusetts Institute of Technology)
