Speaker
Jim Brooke
(University of Bristol (GB))
Description
We describe a CNN based approach to full event classification, for fast first level event selection at the HL-LHC. We perform hardware-aware optimisation of the network architecture, and evaluate physics performance using simulated data. This allowed a range of network models to be identified that fit within target FPGA resources and latency requirements of HL-LHC trigger systems. A candidate model that can be implemented in the CMS L1 trigger is shown to be capable of excellent signal/background discrimination for a high profile HL-LHC physics signal, a pair of Higgs bosons decaying to 4 b-quarks : HH(bbbb). However, the performance depends strongly on the degree of pile-up mitigation prior to image generation.
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Authors
Emyr Clement
(University of Bristol (GB))
Jeronimo Antonio Segal
(University of Bristol (GB))
Jim Brooke
(University of Bristol (GB))
Maciej Mikolaj Glowacki
(CMS)
Sudarshan Paramesvaran
(University of Bristol (GB))