An FPGA based track finder for the L1 trigger of the CMS experiment at the High Luminosity LHC
File(s)Aggleton_2017_J._Inst._12_P12019.pdf (4.4 MB)
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Author(s)
Type
Journal Article
Abstract
A new tracking detector is under development for use by the CMS experiment at the High-Luminosity LHC (HL-LHC). A crucial requirement of this upgrade is to provide the ability to reconstruct all charged particle tracks with transverse momentum above 2–3 GeV within 4 μs so they can be used in the Level-1 trigger decision. A concept for an FPGA-based track finder using a fully time-multiplexed architecture is presented, where track candidates are reconstructed using a projective binning algorithm based on the Hough Transform, followed by a combinatorial Kalman Filter. A hardware demonstrator using MP7 processing boards has been assembled to prove the entire system functionality, from the output of the tracker readout boards to the reconstruction of tracks with fitted helix parameters. It successfully operates on one eighth of the tracker solid angle acceptance at a time, processing events taken at 40 MHz, each with up to an average of 200 superimposed proton-proton interactions, whilst satisfying the latency requirement. The demonstrated track-reconstruction system, the chosen architecture, the achievements to date and future options for such a system will be discussed.
Date Issued
2017-12-14
Date Acceptance
2017-11-23
Citation
Journal of Instrumentation, 2017, 12
ISSN
1748-0221
Publisher
IOP Publishing
Journal / Book Title
Journal of Instrumentation
Volume
12
Copyright Statement
Published by IOP Publishing Ltd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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Subjects
Science & Technology
Technology
Instruments & Instrumentation
Data reduction methods
Digital electronic circuits
Particle tracking detectors
Pattern recognition
cluster finding
calibration and fitting methods
Publication Status
Published
Article Number
P12019