Work

FPGA Implementation of a CNN-Based Topological Trigger for HL-LHC

Year: 2025

Type: article

Abstract: The implementation of convolutional neural networks in programmable logic, for applications in fast online event selection at hadron colliders, is studied. In particular, an approach based on full event images for classification is studied, including hardware-aware optimisation of the network architecture, and evaluation of physics performance using simulated data. A range of network models are identified that can be implemented within resources of current FPGAs, as well as the stringent latency requirements of HL-LHC trigger systems. A candidate model that can be implemented in the CMS L1 trigger for HL-LHC is shown to be capable of excellent signal/background discrimination for a key HL-LHC channel, HH(bbbb), although the performance depends strongly on the degree of pile-up mitigation prior to image generation.

Source: Computing and Software for Big Science

DOI: 10.1007/s41781-025-00150-7

Authors: J. Brooke, E. Clement, M. Glowacki, S. Paramesvaran, J. Segal

Institutions: University of Bristol, European Organization for Nuclear Research

Language: English

Topic: Particle Detector Development and Performance

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,790

Funders: Engineering and Physical Sciences Research Council, Science and Technology Facilities Council