Compact Muon Solenoid Detector Research Articles

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

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).

Pressure correction study for the CMS iRPC detector

The improved Resistive Plate Chambers (iRPC) are designed using thin low resistivity High-Pressure Laminate (HPL) gaps. They are proposed to equip the very forward region of the Compact Muon Solenoid (CMS) detector, as they can stand rates ∼2kHz/cm2. To withstand 3 times higher rates than the installed CMS RPC chambers, the HPL electrode thickness was reduced from 2 mm to 1.4 mm. The gas gain of the detector is dependent on the gas pressure and temperature which requires correcting for the applied voltage to keep detector operational characteristics such as efficiency, cluster size and noise rate constant. Herein, we study the pressure correction at constant temperature for CMS iRPC and compare its correction coefficient with the one for the 2 mm RPC gap technology. Pressure correction parameters for both technologies are found compatible.

Calculation of Forces to the High Granularity Calorimeter Stainless Steel Absorber Plates in the CMS Magnetic Field

The general-purpose Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC) at CERN incorporates a hadronic calorimeter to register the energies of the charged and neutral hadrons produced in proton–proton collisions at the LHC at a center-of-mass energy of 13.6 TeV. This calorimeter is located inside a superconducting solenoid that is 6 m in diameter and 12.5 m in length, generating a central magnetic flux density of 3.8 T. For operating optimally in the high pileup and high radiation environment of the High-Luminosity LHC, the existing CMS endcap calorimeters will be replaced with a new high granularity calorimeter (HGCal) with an electromagnetic section and a hadronic section in each of the two endcaps. The hadronic section of the HGCal will include 44 stainless-steel absorber plates with a relative permeability value well below 1.05. The volume occupied by 22 plates in each endcap is about 21 m3. The calculation of the axial electromagnetic forces acting on the absorber plates is a crucial element in designing the mechanical construction of the device. With a three-dimensional computer model of the CMS magnet, the axial forces on each absorber plate were calculated, and the dependence of forces on the central magnetic flux density value is presented. The method of calculation and the obtained results are discussed.

Heavy-ion physics with Compact Muon Solenoid detector

Heavy-ion physics with Compact Muon Solenoid detector

Upgrade of the Compact Muon Solenoid (CMS) Detector

Upgrade of the Compact Muon Solenoid (CMS) Detector

Development of the CMS Magnetic Field Map

This article focuses on pioneering work on the performance of the three-dimensional (3D) magnetic field map in the entire volume of the Compact Muon Solenoid (CMS) detector at the Large Hadron Collider at CERN. In the CMS heterogeneous magnetic system, the magnetic flux is created by a superconducting solenoid coil enclosed in a steel flux-return yoke. To describe the CMS magnetic flux distribution, a system of the primitive 3D volumes containing the values of the magnetic flux density measured inside the superconducting coil inner volume and modelled outside the coil across a special mesh of reference nodes was developed. This system, called the CMS magnetic field map, follows the geometric features of the yoke and allows the interpolation of the magnetic flux density between the nodes to obtain the magnetic field values at any spatial point inside a cylinder of 18 m in diameter and 48 m in length, where all the CMS sub-detectors are located. The geometry of the volumes is described inside one 30° azimuthal sector of the CMS magnet. To obtain the values of the magnetic flux density components across the entire azimuth angle of the CMS detector, rotational symmetry is applied.

Simulation and Software Integration of the CMS MIP Timing Detector for High-Luminosity LHC

The Compact Muon Solenoid detector at the Large Hadron Collider is undertaking an upgrade program in order to face the harsh conditions foreseen by the High-Luminosity era. This program comprises the installation of a new timing detector whose aim is to measure the time of MIPs, the minimum ionizing particles, with a resolution of around 30-40 ps. The time information provided by this new MIP Timing Detector will improve the rejection of spurious tracks and vertices, will enable particle identification based on the time of flight, and will bring unique physics opportunities for interesting signatures such as those including long-lived particles. All these capabilities require a full software infrastructure to simulate the timing detector and its digitization process, for locally reconstructing the time information associated with tracks, propagating it to the beam line, and contributing to the vertex building. In this paper, the main characteristics of this infrastructure and its integration into the offline software chain are discussed.

The DAQ and clock distribution system of CMS MIP Timing Detector

The Compact Muon Solenoid (CMS) detector at the CERN Large Hadron Collider (LHC) is undergoing an extensive Phase-II upgrade program to cope with the challenging conditions of the High-Luminosity LHC (HL-LHC). A new timing detector is designed to measure minimum ionizing particles (MIPs) with a time resolution of 30–60 ps during the entire HL-LHC phase. A common data acquisition (DAQ) system will collect data from readout chips, reconstruct timing information, and send data to the event builder. The MIP timing detector (MTD) DAQ system is built around the state-of-the-art ATCA-form-factor Serenity board with two high-speed FPGAs. The precision clock is synchronized to the LHC collision rate of 40 MHz and is received at the subsystem and transmitted to the detector via high-speed data links. The detector system with a full-readout chain has been tested using prototypes of the DAQ, on-detector electronics, and sensors, showing that the system can successfully achieve timing resolution below 30 ps.

Multi-Jet Event classification with Convolutional neural network at Large Scale

We present an application of Scalable Deep Learning to analyze simulation data of the LHC proton-proton collisions at 13 TeV. We built a Deep Learning model based on the Convolutional Neural Network (CNN) which utilizes detector responses as two-dimensional images reflecting the geometry of the Compact Muon Solenoid (CMS) detector. The model discriminates signal events of the R-parity violating Supersymmetry (RPV SUSY) from the background events with multiple jets due to the inelastic QCD scattering (QCD multi-jets). With the CNN model, we obtained x1.85 efficiency and x1.2 expected significance with respect to the traditional cut-based method. We demonstrated the scalability of the model at a Large Scale with the High-Performance Computing (HPC) resources at the Korea Institute of Science and Technology Information (KISTI) up to 1024 nodes.

Machine Learning applications for Data Quality Monitoring and Data Certification within CMS

The Compact Muon Solenoid (CMS) detector is getting ready for datataking in 2022, after a long shutdown period. LHC Run-3 is expected to deliver an ever-increasing amount of data. To ensure that the recorded data has the best quality possible, the CMS Collaboration has dedicated Data Quality Monitoring (DQM) and Data Certification (DC) working groups. These working groups are made of human shifters and experts who carefully watch and investigate histograms generated from different parts of the detector. However, the current workflow is not granular enough and prone to human errors. On the other hand, several techniques in Machine Learning (ML) can be designed to learn from large collections of data and make predictions for the data quality at an unprecedented speed and granularity. Hence, the data certification process can be considered as a perfect problem for ML techniques to tackle. With the help of ML, we can increase the granularity and speed of the DQM workflow and assist the human shifters and experts in detecting anomalies during data-taking. In this presentation, we present preliminary results from incorporating ML to highly granular DQM information for data certification.

Transient optical absorption technique to test timing properties of LYSO:Ce scintillators for the CMS Barrel Timing Layer

A novel technique for testing the timing properties of scintillators is presented. The technique is based on transient absorption (TA) induced in a scintillating material by a selective excitation of the activator ion. A figure of merit to assess the timing properties of scintillators is suggested. This parameter was estimated for a set of cerium doped lutetium–yttrium oxyorthosilicate (LYSO:Ce) bars, which have been fabricated for Barrel Timing Layer sensor of Compact Muon Solenoid detector (CMS BTL) and exhibited different timing properties, and compared with the results obtained by conventional coincidence time resolution (CTR) measurements. The figure of merit applied for the tested bars shows a strong correlation (Pearson's correlation coefficient R = 0.95) with the CTR. These results suggest that the TA technique could be used as an experimental method to expand in a complementary way the extensive qualification procedure of LYSO:Ce crystals that will be performed for the production of the CMS BTL detector.

Application of Machine Learning Algorithms for Searching BSM Higgs Bosons Decaying to a Pair of Bottom Quarks

Although the discovery of the 125 GeV Higgs boson confirms the Higgs mechanism of the Standard Model (SM), many theories beyond the SM have been introduced to address several phenomena yet to be explained by the SM. For instance, the 2-Higgs Doublet Models is the simplest extension of the SM Higgs sector and predicting the existence of additional Higgs bosons at different states. The aim of this study is to search for machine learning (ML) algorithms which have been widely used in High Energy Physics. This will improve the sensitivity of the search for BSM Higgs bosons produced in association with a bottom quark () that then decays into a pair of bottom quarks (); the predominant decay channel of the Higgs boson, though, buried by a large multi-jet background process. In this study, we train 2 different ML algorithms: Tree-based models and Neural Networks, to classify signal and background events collected by the Compact Muon Solenoid detector from proton-proton collisions at 13 TeV. The evaluation metrics are calculated to provide classification efficiencies from different models. The results show that the classification of signal and background processes can be improved using ML techniques. Neural Networks reported the highest AUC score of 0.951 which is comparable with Adaptive Boosting model, while Decision Trees (DTs) and Random Forest models slightly underperformed by 2 - 3 %. We therefore can make use of the trained models as signal vs background classifiers to perform further statistical analysis searches for BSM Higgs bosons. GRAPHICAL ABSTRACT

Influence of the high granularity calorimeter stainless steel absorbers onto the Compact Muon Solenoid inner magnetic field

The Compact Muon Solenoid (CMS) detector is a general-purpose experimental setup at the Large Hadron Collider (LHC) at CERN to investigate the production of new particles in the proton-proton collisions at a centre of mass energy 13 TeV. The third run of the data taken is started in April 2022 and will continue till the end of 2025. Then, during a long shutdown time, the existing CMS hadron endcap calorimeter will be replaced with a new high granularity calorimeter (HGCal) designed for the higher LHC luminosity. The HGCal contains the stainless-steel absorber plates with a relative permeability limited by a value of 1.05 from estimation of the electromagnetic forces acting on this slightly magnetic material. To exclude the surprises with possible perturbation of the inner magnetic flux density in the region of the charged particle tracking system, an influence of this additional material onto the quality of the magnetic field inside the inner tracker volume is investigated at this limited value of the permeability of stainless steel. The three-dimensional model of the CMS magnet is used for this purpose. The method of the magnetic field double integrals characterizing the charged particle momentum resolution the first time is applied to the CMS detector and the first time is described in the journal publication. The results obtained with this method are presented in detail and discussed.

The upgrade of the CMS electromagnetic calorimeter: Future prospects for precision timing and energy measurements at the HL-LHC

The Compact Muon Solenoid (CMS) electromagnetic calorimeter (ECAL) is a high granularity lead tungstate crystal calorimeter operated at the CERN LHC. The future High Luminosity upgrade of the LHC will provide unprecedented instantaneous and integrated luminosity with up to 200 collisions per bunch-crossing [pileup (PU)]. The CMS detector is therefore undergoing an extensive upgrade program to prepare for these demanding conditions. In the ECAL barrel region, the crystals will continue to perform well. The Avalanche Photodiodes (APDs) detecting the scintillation light will also continue to be operational, with some increase in noise that will be mitigated by reducing the operational temperature. Nonetheless, the entire readout and trigger electronics will need to be replaced to cope with the harsh conditions and increased trigger latency requirements. We plan a 25-fold improved readout granularity and a factor 4 sampling rate increase. The timing precision is used in important physics measurements and it is speculated that further improved time information could be exploited for PU mitigation and for the photons assignment to the correct collision vertex. As presented in this paper the endcaps region calorimeter needs to be replaced due to irrecoverable radiation damage of the crystals in the forward region. The upgraded ECAL will preserve the energy resolution, and will improve on time resolution for photons and electrons. In this paper, the ongoing R&D activities for the ECAL barrel readout electronics upgrade are presented.

The CMS Magnetic Field Measuring and Monitoring Systems

This review article describes the performance of the magnetic field measuring and monitoring systems for the Compact Muon Solenoid (CMS) detector. To cross-check the magnetic flux distribution obtained with the CMS magnet model, four systems for measuring the magnetic flux density in the detector volume were used. The magnetic induction inside the 6 m diameter superconducting solenoid was measured and is currently monitored by four nuclear magnetic resonance (NMR) probes installed using special tubes at a radius of 2.9148 m outside the barrel hadron calorimeter at ±0.006 m from the coil median XY-plane. Two more NRM probes were installed at the faces of the tracking system at Z-coordinates of −2.835 and +2.831 m and a radius of 0.651 m from the solenoid axis. The field inside the superconducting solenoid was precisely measured in 2006 in a cylindrical volume of 3.448 m in diameter and 7 m in length using ten three-dimensional (3D) B-sensors based on the Hall effect (Hall probes). These B-sensors were installed on each of the two propeller arms of an automated field-mapping machine. In addition to these measurement systems, a system for monitoring the magnetic field during the CMS detector operation has been developed. Inside the solenoid in the horizontal plane, four 3D B-sensors were installed at the faces of the tracking detector at distances X = ±0.959 m and Z-coordinates of −2.899 and +2.895 m. Twelve 3D B-sensors were installed on the surfaces of the flux-return yoke nose disks. Seventy 3D B-sensors were installed in the air gaps of the CMS magnet yoke in 11 XY-planes of the azimuthal sector at 270°. A specially developed flux loop technique was used for the most complex measurements of the magnetic flux density inside the steel blocks of the CMS magnet yoke. The flux loops are installed in 22 sections of the flux-return yoke blocks in grooves of 30 mm wide and 12–13 mm deep and consist of 7–10 turns of 45 wire flat ribbon cable. The areas enclosed by these coils varied from 0.3 to 1.59 m2 in the blocks of the barrel wheels and from 0.5 to 1.12 m2 in the blocks of the yoke endcap disks. The development of these systems and the results of the magnetic flux density measurements across the CMS magnet are presented and discussed in this review article.

Performance of a triple-GEM demonstrator in pp collisions at the CMS detector

After the Phase-2 high-luminosity upgrade to the Large Hadron Collider (LHC), the collision rate and therefore the background rate will significantly increase, particularly in the high η region. To improve both the tracking and triggering of muons, the Compact Muon Solenoid (CMS) Collaboration plans to install triple-layer Gas Electron Multiplier (GEM) detectors in the CMS muon endcaps. Demonstrator GEM detectors were installed in CMS during 2017 to gain operational experience and perform a preliminary investigation of detector performance. We present the results of triple-GEM detector performance studies performed in situ during normal CMS and LHC operations in 2018. The distribution of cluster size and the efficiency to reconstruct high pT muons in proton-proton collisions are presented as well as the measurement of the environmental backgroundrate to produce hits in the GEM detector.

Design and Description of the CMS Magnetic System Model

This review describes the composition of the Compact Muon Solenoid (CMS) detector and the methodology for modelling the heterogeneous CMS magnetic system, starting with the formulation of the magnetostatics problem for modelling the magnetic flux of the CMS superconducting solenoid enclosed in a steel flux-return yoke. The review includes a section on the magnetization curves of various types of steel used in the CMS magnet yoke. The evolution of the magnetic system model over 20 years is presented in the discussion section and is well illustrated by the CMS model layouts and the magnetic flux distribution.

Research and development of the back-end electronics for the two-dimensional improved resistive plate chambers in CMS upgrade

To complement and ensure redundancy in the endcap muon system of the Compact Muon Solenoid (CMS) detector and to extend the Resistive Plate Chamber (RPC) system coverage, improved RPCs (iRPCs) with either orthogonal layer strips with one-end electronics or single layer strips with two-end electronics providing more precise time measurement will be installed in the very forward pseudorapidity region of $$|\eta |<2.4$$ . The iRPC readout system needs to support two-dimensional (2D) or two-end readout. In addition, it must combine detector data with Timing, Trigger and fast Control (TTC) and Slow Control (SC) into one data stream over a bi-directional optical link with a line rate of 4.8 Gb/s between the Front-End Electronics (FEE) and the Back-End Electronics (BEE). To fulfill these requirements, a prototype BEE for the iRPC 2D chamber has been researched and designed. A Micro-Telecommunication and Computing Architecture ( $$\mu $$ TCA)-based processing card was designed in this study to establish a prototype system together with a $$\mu $$ TCA crate. The Giga-Bit Transceiver (GBT) protocol is integrated to provide bi-directional communication between the FEE and BEE. A server is connected with the BEE by a Gigabit Ethernet (GbE) link for SC and a 10-GbE link for Data AcQuisition (DAQ). The Bit Error Rate (BER) test of the back-end board and a joint test with the iRPC 2D prototype chamber were performed. A BER of less than $$1.331\times {10^{-16}}$$ was obtained. The time measurement with a resolution of 3.05 ns was successfully realized, and detector efficiencies of 97.7% for longitudinal strips and 96.0% for orthogonal strips were measured. Test results demonstrate the correctness and reliability of the prototype BEE. The BEE prototype satisfies the requirements for the iRPC 2D chamber, and it worked stably and reliably during a long-term joint test run.

Program of Searches with the CMS Detector for Signals from Multidimensional Low-Energy Gravity at the Large Hadron Collider

This article, presented on behalf of the Compact Muon Solenoid (CMS) Collaboration, gives a generalizing survey of the results of the CMS experiment that concern searches for Kaluza–Klein excited states of the graviton (KK modes) and for microscopic multidimensional black holes, quantum black holes, and string balls within multidimensional low-energy gravity. The present analysis relies on data obtained during the first (2010–2012) and second (2015–2018) stages of operation of the Large Hadron Collider (LHC) in proton–proton collisions at the center-of-mass energies of 7, 8, and 13 TeV. The results of experimental searches are interpreted in terms of constraints on the space of parameters of the theoretical models being studied. Also, further prospects for the HL-LHC mode and for the possible Future Circular Collider (FCC) are discussed briefly.

Heavy-Ion Physics with the Compact Muon Solenoid (CMS) Detector at the Large Hadron Collider (LHC)

The present article gives a brief account of some interesting results obtained most recently in studying heavy-ion physics—in particular, quark–gluon matter—with the Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC).