Hayrapetyan, A.; Makarenko, V.; Tumasyan, A.; Adam, W.; Andrejkovic, J.W.; Benato, L.; Bergauer, T.; Dragicevic, M.; Giordano, C.; Hussain, P.S.

A tau lepton identification algorithm, DeepTau, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons (τh) from quark or gluon jets and electrons and muons that are misreconstructed as τh candidates. The latest version of this algorithm, v2.5, includes domain adaptation by backpropagation, a technique that reduces discrepancies between collision data and simulation in the region with the highest purity of genuine τh candidates. Additionally, a refined training workflow improves classification performance with respect to the previous version of the algorithm, with a reduction of 30–50% in the probability for quark and gluon jets to be misidentified as τh candidates for given reconstruction and identification efficiencies. This paper presents the novel improvements introduced in the DeepTau algorithm and evaluates its performance in LHC proton-proton collision data at √s = 13 and 13.6 TeV collected in 2018 and 2022 with integrated luminosities of 60 and 35 fb−1, respectively. Techniques to calibrate the performance of the τh identification algorithm in simulation with respect to its measured performance in real data are presented, together with a subset of results among those measured for use in CMS physics analyses.

Hayrapetyan, A.; Tumasyan, A.; Adam, W.; Andrejkovic, J.W.; Benato, L.; Bergauer, T.; Chatterjee, S.; Damanakis, K.; Dragicevic, M.; Hussain, P.S.

Measurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to (Formula presented) or (Formula presented), have been developed and deployed across a range of physics analyses. This paper highlights their performance on simulated events, and summarizes novel calibration techniques using proton-proton collision data collected at (Formula presented) during the 2016–2018 LHC data-taking period. Three dedicated methods are used for the calibration in multijet events, leveraging either machine learning techniques, the presence of muons within energetic boosted jets, or the reconstruction of hadronically decaying high-energy Z bosons. The calibration results, obtained through a combination of these approaches, are presented and discussed.

Chekhovsky, V.; Hayrapetyan, A.; Makarenko, V.; Tumasyan, A.; Adam, W.; Andrejkovic, J.W.; Benato, L.; Bergauer, T.; Chatterjee, S.; Damanakis, K.

"Soft"muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously suppressing backgrounds from long-lived light-flavor hadron decays, is therefore important for the success of the CMS physics program. Multivariate techniques have been shown to deliver better muon identification performance than traditional selection techniques. To take full advantage of the large data set currently being collected during Run 3 of the CERN LHC, a new multivariate classifier based on a gradient-boosted decision tree has been developed. It offers a significantly improved separation of signal and background muons compared to a similar classifier used for the analysis of the Run 2 data. The performance of the new classifier is evaluated on a data set collected with the CMS detector in 2022 and 2023, corresponding to an integrated luminosity of 62 fb-1

Hayrapetyan, A.; Tumasyan, A.; Adam, W.; Andrejkovic, J.W.; Benato, L.; Bergauer, T.; Chatterjee, S.; Damanakis, K.; Dragicevic, M.; Hussain, P.S.

The TOTEM Roman pot detectors are used to reconstruct the transverse momentum of scattered protons and to estimate the transverse location of the primary interaction. This paper presents new methods of track reconstruction, measurements of strip-level detection efficiencies, cross-checks of the LHC beam optics, and detector alignment techniques, along with their application in the selection of signal collision events. The track reconstruction is performed by exploiting hit cluster information through a novel method using a common polygonal area in the intercept-slope plane. The technique is applied in the relative alignment of detector layers with μm precision. A tag-and-probe method is used to extract strip-level detection efficiencies. The alignment of the Roman pot system is performed through time-dependent adjustments, resulting in a position accuracy of 3 μm in the horizontal and 60 μm in the vertical directions. The goal is to provide an optimal reconstruction tool for central exclusive physics analyses based on the high-β∗ data-taking period at √s = 13 TeV in 2018.

Hayrapetyan, A.; Tumasyan, A.; Adam, W.; Andrejkovic, J.W.; Bergauer, T.; Chatterjee, S.; Damanakis, K.; Dragicevic, M.; Escalante Del Valle, A.; Hussain, P.S.

The identification of prompt and isolated muons, as well as muons from heavy-flavour hadron decays, is an important task. We developed two multivariate techniques to provide highly efficient identification for muons with transverse momentum greater than 10 GeV. One provides a continuous variable as an alternative to a cut-based identification selection and offers a better discrimination power against misidentified muons. The other one selects prompt and isolated muons by using isolation requirements to reduce the contamination from nonprompt muons arising in heavy-flavour hadron decays. Both algorithms are developed using 59.7 fb−1 of proton-proton collisions data at a centre-of-mass energy of √s = 13 TeV collected in 2018 with the CMS experiment at the CERN LHC.

Hayrapetyan, A.; Tumasyan, A.; Adam, W.; Andrejkovic, J.W.; Arnold, B.; Bergauer, H.; Bergauer, T.; Chatterjee, S.; Damanakis, K.; Dragicevic, M.

Since the initial data taking of the CERN LHC, the CMS experiment has undergone substantial upgrades and improvements. This paper discusses the CMS detector as it is configured for the third data-taking period of the CERN LHC, Run 3, which started in 2022. The entire silicon pixel tracking detector was replaced. A new powering system for the superconducting solenoid was installed. The electronics of the hadron calorimeter was upgraded. All the muon electronic systems were upgraded, and new muon detector stations were added, including a gas electron multiplier detector. The precision proton spectrometer was upgraded. The dedicated luminosity detectors and the beam loss monitor were refurbished. Substantial improvements to the trigger, data acquisition, software, and computing systems were also implemented, including a new hybrid CPU/GPU farm for the high-level trigger.

Hayrapetyan, A.; Tumasyan, A.; Adam, W.; Andrejkovic, J.; Bergauer, T.; Chatterjee, S.; Damanakis, K.; Dragicevic, M.; Escalante Del Valle, A.; Hussain, P.

The operation and performance of the Compact Muon Solenoid (CMS) electromagnetic calorimeter (ECAL) are presented, based on data collected in pp collisions at √s = 13 TeV at the CERN LHC, in the years from 2015 to 2018 (LHC Run 2), corresponding to an integrated luminosity of 151 fb-1. The CMS ECAL is a scintillating lead-tungstate crystal calorimeter, with a silicon strip preshower detector in the forward region that provides precise measurements of the energy and the time-of-arrival of electrons and photons. The successful operation of the ECAL is crucial for a broad range of physics goals, ranging from observing the Higgs boson and measuring its properties, to other standard model measurements and searches for new phenomena. Precise calibration, alignment, and monitoring of the ECAL response are important ingredients to achieve these goals. To face the challenges posed by the higher luminosity, which characterized the operation of the LHC in Run 2, the procedures established during the 2011-2012 run of the LHC have been revisited and new methods have been developed for the energy measurement and for the ECAL calibration. The energy resolution of the calorimeter, for electrons from Z boson decays reaching the ECAL without significant loss of energy by bremsstrahlung, was better than 1.8%, 3.0%, and 4.5% in the |η| intervals [0.0,0.8], [0.8,1.5], [1.5, 2.5], respectively. This resulting performance is similar to that achieved during Run 1 in 2011-2012, in spite of the more severe running conditions.

Hayrapetyan, A.; Tumasyan, A.; Adam, W.; Andrejkovic, J.W.; Bergauer, T.; Chatterjee, S.; Damanakis, K.; Dragicevic, M.; Hussain, P.S.; Jeitler, M.

The performance of muon tracking, identification, triggering, momentum resolution, and momentum scale has been studied with the CMS detector at the LHC using data collected at √s NN= 5.02 TeV in proton-proton (pp) and lead-lead (PbPb) collisions in 2017 and 2018, respectively, and at √s NN= 8.16 TeV in proton-lead (pPb) collisions in 2016. Muon efficiencies, momentum resolutions, and momentum scales are compared by focusing on how the muon reconstruction performance varies from relatively small occupancy pp collisions to the larger occupancies of pPb collisions and, finally, to the highest track multiplicity PbPb collisions. We find the efficiencies of muon tracking, identification, and triggering to be above 90% throughout most of the track multiplicity range. The momentum resolution and scale are unaffected by the detector occupancy. The excellent muon reconstruction of the CMS detector enables precision studies across all available collision systems.

Hayrapetyan, A.; Tumasyan, A.; Adam, W.; Andrejkovic, J.W.; Benato, L.; Bergauer, T.; Chatterjee, S.; Damanakis, K.; Dragicevic, M.; Hussain, P.S.

The CERN LHC provided proton and heavy ion collisions during its Run 2 operation period from 2015 to 2018. Proton-proton collisions reached a peak instantaneous luminosity of 2.1 × 1034 cm-2s-1, twice the initial design value, at √(s)=13 TeV. The CMS experiment records a subset of the collisions for further processing as part of its online selection of data for physics analyses, using a two-level trigger system: the Level-1 trigger, implemented in custom-designed electronics, and the high-level trigger, a streamlined version of the offline reconstruction software running on a large computer farm. This paper presents the performance of the CMS high-level trigger system during LHC Run 2 for physics objects, such as leptons, jets, and missing transverse momentum, which meet the broad needs of the CMS physics program and the challenge of the evolving LHC and detector conditions. Sophisticated algorithms that were originally used in offline reconstruction were deployed online. Highlights include a machine-learning b tagging algorithm and a reconstruction algorithm for tau leptons that decay hadronically.

Tumasyan, A.; Adam, W.; Andrejkovic, J.W.; Bergauer, T.; Chatterjee, S.; Damanakis, K.; Dragicevic, M.; Escalante Del Valle, A.; Hussain, P.S.; Jeitler, M.

A description is presented of the algorithms used to reconstruct energy deposited in the CMS hadron calorimeter during Run 2 (2015-2018) of the LHC. During Run 2, the characteristic bunch-crossing spacing for proton-proton collisions was 25 ns, which resulted in overlapping signals from adjacent crossings. The energy corresponding to a particular bunch crossing of interest is estimated using the known pulse shapes of energy depositions in the calorimeter, which are measured as functions of both energy and time. A variety of algorithms were developed to mitigate the effects of adjacent bunch crossings on local energy reconstruction in the hadron calorimeter in Run 2, and their performance is compared.