Antoni Shtipliyski

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.

A new tracking system is under development for operation in the CMS experiment at the High Luminosity LHC. It includes an outer tracker which will construct stubs, built by correlating clusters in two closely spaced sensor layers for the rejection of hits from low transverse momentum tracks, and transmit them off-detector at 40 MHz. If tracker data is to contribute to keeping the Level-1 trigger rate at around 750 kHz under increased luminosity, a crucial component of the upgrade will be the ability to identify tracks with transverse momentum above 3 GeV/c by building tracks out of stubs. A concept for an FPGA-based track finder using a fully time-multiplexed architecture is presented, where track candidates are identified using a projective binning algorithm based on the Hough Transform. A hardware system based on the MP7 MicroTCA processing card has been assembled, demonstrating a realistic slice of the track finder in order to help gauge the performance and requirements for a full system. This paper outlines the system architecture and algorithms employed, highlighting some of the first results from the hardware demonstrator and discusses the prospects and performance of the completed track finder.

Abstract The CMS experiment will be upgraded to maintain physics sensitivity and exploit the improved performance of the High Luminosity LHC. Part of this upgrade will see the first level (Level-1) trigger use charged particle tracks reconstructed within the full outer silicon tracker volume as an input for the first time and new algorithms are being designed to make use of these tracks. One such algorithm is primary vertex finding which is used to identify the hard scatter in an event and separate the primary interaction from additional simultaneous interactions. This work presents a novel approach to regress the primary vertex position and to reject tracks from additional soft interactions, which uses an end-to-end neural network. This neural network possesses simultaneous knowledge of all stages in the reconstruction chain, which allows for end-to-end optimisation. The improved performance of this network versus a baseline approach in the primary vertex regression and track-to-vertex classification is shown. A quantised and pruned version of the neural network is deployed on an FPGA to match the stringent timing and computing requirements of the Level-1 Trigger.

Results from the completed Phase 1 Upgrade of the Compact Muon Solenoid (CMS) Level-1 Calorimeter Trigger are presented. The upgrade was performed in two stages, with the first running in 2015 for proton and heavy ion collisions and the final stage for 2016 data taking. The Level-1 trigger has been fully commissioned and has been used by CMS to collect over 43 fb−1 of data since the start of the Run II of the Large Hadron Collider (LHC). The new trigger has been designed to improve the performance at high luminosity and large number of simultaneous inelastic collisions per crossing (pile-up). For this purpose it uses a novel design, the Time Multiplexed Trigger (TMT), which enables the data from an event to be processed by a single trigger processor at full granularity over several bunch crossings. The TMT design is a modular design based on the μTCA standard. The trigger processors are instrumented with Xilinx Virtex-7 690 FPGAs and 10 Gbps optical links. The TMT architecture is flexible and the number of trigger processors can be expanded according to the physics needs of CMS. Sophisticated and innovative algorithms are now the core of the first decision layer of the experiment. The system has been able to adapt to the outstanding performance of the LHC, which ran with an instantaneous luminosity well above design. The performance of the system for single physics objects are presented along with the optimizations foreseen to maintain the thresholds for the harsher conditions expected during the LHC Run II and Run III periods.

The Compact Muon Solenoid (CMS) employs a sophisticated two-level online triggering system that has a rejection factor of up to 105. Since the beginning of Run II of LHC, the conditions that CMS operates in have become increasingly challenging. The centre-of-mass energy is now 13 TeV and the instantaneous luminosity currently peaks at 1.5 ×1034 cm−2s−1. In order to keep low physics thresholds and to trigger efficiently in such conditions, the CMS trigger system has been upgraded. A new trigger architecture, the Time Multiplexed Trigger (TMT) has been introduced which allows the full granularity of the calorimeters to be exploited at the first level of the online trigger. The new trigger has also benefited immensely from technological improvements in hardware. Sophisticated algorithms, developed to fully exploit the advantages provided by the new hardware architecture, have been implemented. The new trigger system started taking physics data in 2016 following a commissioning period in 2015, and since then has performed extremely well. The hardware and firmware developments, electron and photon algorithms together with their performance in challenging 2016 conditions is presented.

The Compact Muon Solenoid (CMS) experiment at CERN is scheduled for a major upgrade in the next decade in order to meet the demands of the new High Luminosity Large Hadron Collider.Amongst others, a new tracking system is under development including an outer tracker capable of rejecting low transverse momentum particles by looking at the coincidences of hits (stubs) in two closely spaced sensor layers in the same tracker module.Accepted stubs are transmitted off-detector for further processing at 40 MHz.In order to maintain under the increased luminosity the Level-1 trigger rate at 750 kHz, tracker data need to be included in the decision making process.For this purpose, a system architecture has to be developed that will be able to identify particles with transverse momentum above 3 GeV/c by building tracks out of stubs, while achieving an overall processing latency of maximum 4us.Targeting these requirements the current paper presents an FPGA-based track finding architecture that identifies track candidates in real-time and bases its functionality on a fully time-multiplexed approach.As a proof of concept, a hardware system has been assembled targeting the MP7 MicroTCA processing card that features a Xilinx Virtex-7 FPGA, demonstrating a realistic slice of the track finder.The paper discusses the algorithms' implementation and the efficient utilisation of the available FPGA resources, it outlines the system architecture, and presents some of the hardware demonstrator results.

The CMS collaboration is preparing a major upgrade of its detector, so it can operate during the high luminosity run of the LHC from 2026. The upgraded tracker electronics will reconstruct the trajectories of charged particles within a latency of a few microseconds, so that they can be used by the level-1 trigger. An emulation framework, CIDAF, has been developed to provide a reference for a proposed FPGA-based implementation of this track finder, which employs a Time-Multiplexed (TM) technique for data processing.

A new tracking detector is under development for use by the CMS experiment at the High-Luminosity LHC (HL-LHC).A crucial component of this upgrade will be the ability to reconstruct within a few microseconds all charged particle tracks with transverse momentum above 3 GeV, 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 track fitting based on the linear regression technique.A hardware demonstrator using MP7 processing boards has been assembled to prove the entire system, 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 at a time, processing events taken at 40 MHz, each with up to 200 superimposed proton-proton interactions, whilst satisfying latency constraints.The demonstrated track-reconstruction system, the chosen architecture, the achievements to date and future options for such a system will be discussed.