The Large Hadron Collider (LHC) is being prepared for an extensive upgrade to boost its particle discovery potential. The new phase, High Luminosity LHC, will operate at a factor-of-five-increased luminosity (the number proportional to the rate of collisions). Consequently, such an increase in luminosity will result in enormous quantities of generated data that cannot be transmitted or stored with the currently available resources and time. However, the vast majority of the generated data consist of uninteresting data or pile-up data containing few interesting events or electromagnetic showers. High-Luminosity LHC detectors, including the Compact Muon Solenoid (CMS), will thus have to rely on innovative approaches like the proposed one to select interesting collision data. In charge of data reduction/selection at the early stages of data streaming is a level 1 trigger (L1T), a real-time event selection system. The final step of the L1T is a global trigger, which uses sub-system algorithms to make a final decision about signal acceptance/rejection within a decision time of around 12 microseconds. For one of these sub-system L1T algorithms, we propose using quantized neural network models deployed in targeted L1T devices, namely, field-programmable gate arrays (FPGA), as a classifier between electromagnetic and pile-up/quantum chromodynamics showers. The developed quantized neural network operates in an end-to-end manner using raw detector data to speed up the classification process. The proposed data reduction methods further decrease model size while retaining accuracy. The proposed approach was tested with simulated data (since the detector is still in the production stage) and took less than 1 microsecond, achieving real-time signal–background classification with a classification accuracy of 97.37% for 2-bit-only quantization and 97.44% for quantization augmented with the data reduction approach (compared to 98.61% for the full-precision, standard network ).

Towards Real-Time Machine Learning-Based Signal/Background Selection in the CMS Detector Using Quantized Neural Networks and Input Data Reduction

Abstract The muon system of the CMS experiment is expected to upgrade all of its subdetectors for the Phase-2 of the Large Hadron Collider (LHC) that will begin in 2029. The upgrade plans for drift tubes (DTs), cathode strip chambers (CSCs) and resistive place chambers (RPCs) include a new electronics for better performance in high background rate conditions and to sustain the large radiation dose delivered by the high-luminosity LHC (HL-LHC). Two new RPC stations will also be installed to complement CSCs in the forward region 1.8 &lt; | η | &lt; 2.4, while three stations of triple-GEM (gas electron multiplier) detectors will complement CSCs in the region 1.5 &lt; | η | &lt; 2.4 and extend the CMS muon system coverage to the very forward pseudorapidity region 2.4 &lt; | η | &lt; 2.8. We present the goals and status of the CMS muon system upgrade including the performance of the early Phase-2 upgrades demonstrated in Run 3 with proton-proton collisions, the performance validation of the new detectors in test beams and the mass production status of the new stations. 

The upgrade of the CMS muon system for the high luminosity LHC

Abstract The Level-1 trigger scouting system of the CMS experiment aims at intercepting the intermediate data produced by the L1 trigger processors before the final trigger decision. This system can be complemented by adding the raw stream of data collected from the detector front-end, whenever the throughput is manageable. In this work, the triggerless readout of the CMS Drift Tubes (DT) detector is presented. This is realized by reading a sector of the DT which has been equipped with the preproduction of Phase-2 upgrade front-end boards. A Xilinx VCU118 acts as a concentrator of the Phase-2 demonstrator lpGBT links and transmits data to a server via 100G TCP/IP. First results coming from a test-stand mimicking the sector demonstrator are shown. 

40 MHz triggerless readout of the CMS Drift Tube muon detector

The muon system of the CMS experiment is expected to upgrade all of its subdetectors for the Phase-2 of the Large Hadron Collider (LHC) that will begin in 2029. The upgrade plans for drift tubes (DTs), cathode strip chambers (CSCs) and resistive place chambers (RPCs) include a new electronics for better performance in high background rate conditions and to sustain the large radiation dose delivered by the high-luminosity LHC (HL-LHC). Two new RPC stations will also be installed to complement CSCs in the forward region $1.8<|\eta|<2.4$, while three stations of triple-GEM (gas electron multiplier) detectors will complement CSCs in the region $1.5<|\eta|<2.4$ and extend the CMS muon system coverage in the very forward pseudorapidity region up to $|\eta| = 2.8$. We present the goals and status of the CMS muon system upgrade including the performance of the early Phase-2 upgrades demonstrated in Run 3 with proton-proton collisions, the performance validation of the new detectors in test beams and the mass production status of the new stations.

pdf/the-upgrade-of-the-cms-muon-system-for-the-high-luminosity-11dvnaooku.pdf

The upgrade of the CMS muon system for the High Luminosity LHC

The Compact Muon Solenoid (CMS) experiment prepares its Phase-2 upgrade for the high-luminosity era of the LHC operation (HL-LHC). Due to the increase of occupancy, trigger latency and rates, the full electronics of the CMS Drift Tube (DT) chambers will need to be replaced. In the new design, the time bin for the digitisation of the chamber signals will be of around 1~ns, and the totality of the signals will be forwarded asynchronously to the service cavern at full resolution. The new backend system will be in charge of building the trigger primitives of each chamber. These trigger primitives contain the information at chamber level about the muon candidates position, direction, and collision time, and are used as input in the L1 CMS trigger. The added functionalities will improve the robustness of the system against ageing. An algorithm based on analytical solutions for reconstructing the DT trigger primitives, called Analytical Method, has been implemented both as a software C++ emulator and in firmware. Its performance has been estimated using the software emulator with simulated and real data samples, and through hardware implementation tests. Measured efficiencies are 96 to 98\% for all qualities and time and spatial resolutions are close to the ultimate performance of the DT chambers. A prototype chain of the HL-LHC electronics using the Analytical Method for trigger primitive generation has been installed during Long Shutdown 2 of the LHC and operated in CMS cosmic data taking campaigns in 2020 and 2021. Results from this validation step, the so-called Slice Test, are presented. 

The Analytical Method algorithm for trigger primitives generation at the LHC Drift Tubes detector

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The Analytical Method algorithm for trigger primitives generation at the LHC Drift Tubes detector