Speaker
Mr
David Nonso Ojika
(University of Florida (US))
Description
Leveraging on our previous work on developing DNN-based classification models for Higss events [1], we turn to CNN-based classification models for muon events. Using Intel Knights Landing (KNL) processors, we present performance metrics on training convolutional neural networks (CNNs) on multiple KNL computing nodes for the task of muon identification (i.e "high Pt" or "low Pt"). This work is an improvement over previous similarly tasked workload of using deep neural networks (DNNs) for higgs identification (i.e. "higgs" or "background").
| Intended contribution length | 20 minutes |
|---|
Primary authors
Mr
David Nonso Ojika
(University of Florida (US))
Mr
Akash Vasishta
(University of Florida)
Ms
Chandana Prasad
(University of Florida)
Mr
Chao Jiang
(University of Florida)
Co-authors
Dr
Lam Herman
(University of Florida)
Dr
Darin Acosta
(University of Florida)
Dr
Ann Gordon-Ross
(University of Florida)
