Speaker
Dr
Emanuele Coradin
(University of Padova)
Description
Using a spiking neural network and a modeling of the silicon tracker for the CMS upgraded detector, we demonstrate the unsupervised learning application of identification of charged particle tracks in presence of background, and characterize the detection efficiency, fake rate, and differentiation of output signals for particles of different momenta and charge.
Primary authors
Dr
Emanuele Coradin
(University of Padova)
Dr
Fabio Cufino
(University of Bologna)
Prof.
Fredrik Sandin
(Lulea Techniska Universitet)
Prof.
Mia Tosi
(University of Padova)
Tommaso Dorigo
(Universita e INFN, Padova (IT))
