A. Hayrapetyan’s research while affiliated with A. Alikhanyan National Laboratory and other places

A bstract The measurements of the Higgs boson (H) production cross sections performed by the CMS Collaboration in the four-lepton (4 ℓ, ℓ = e , μ) final state at a center-of-mass energy $\sqrt{s}$ = 13 . 6 TeV are presented. These measurements are based on data collected with the CMS detector at the CERN LHC in 2022, corresponding to an integrated luminosity of 34.7 fb − 1 . Cross sections are measured in a fiducial region closely matching the experimental acceptance, both inclusively and differentially, as a function of the transverse momentum and the absolute value of the rapidity of the four-lepton system. The H → ZZ → 4 ℓ inclusive fiducial cross section is measured to be ${2.89}_{-0.49}^{+0.53}{\left({\text{stat}}\right)}_{-0.21}^{+0.29}\left({\text{syst}}\right)$ fb, in agreement with the standard model expectation of ${3.09}_{-0.24}^{+0.27}$ fb.

Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a geant -based simulation of the detectors are used to produce large samples of simulated events for analysis by the LHC experiments. These simulations come at a high computational cost, where the detector simulation and reconstruction algorithms have the largest CPU demands. This article describes how machine-learning (ML) techniques are used to reweight simulated samples obtained with a given set of parameters to samples with different parameters or samples obtained from entirely different simulation programs. The ML reweighting method avoids the need for simulating the detector response multiple times by incorporating the relevant information in a single sample through event weights. Results are presented for reweighting to model variations and higher-order calculations in simulated top quark pair production at the LHC. This ML-based reweighting is an important element of the future computing model of the CMS experiment and will facilitate precision measurements at the High-Luminosity LHC.

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 .

A bstract A measurement of the top quark pair ( $\textrm{t}\overline{\textrm{t}}$ t t ¯ ) production cross section in proton-proton collisions at a centre-of-mass energy of 5.02 TeV is presented. The data were collected at the LHC in autumn 2017, in dedicated runs with low-energy and low-intensity conditions with respect to the default configuration, and correspond to an integrated luminosity of 302 pb − 1 . The measurement is performed using events with one electron or muon, and multiple jets, at least one of them being identified as originating from a b quark (b tagged). Events are classified based on the number of all reconstructed jets and of b-tagged jets. Multivariate analysis techniques are used to enhance the separation between the signal and backgrounds. The measured cross section is $62.5\pm 1.6{\left(\textrm{stat}\right)}_{-2.5}^{+2.6}\left(\textrm{syst}\right)\pm 1.2\left(\textrm{lumi}\right)$ 62.5 ± 1.6 stat − 2.5 + 2.6 syst ± 1.2 lumi pb. A combination with the result in the dilepton channel based on the same data set yields a value of 62.3 ± 1.5 (stat) ± 2.4 (syst) ± 1.2 (lumi) pb, to be compared with the standard model prediction of ${69.5}_{-3.7}^{+3.5}$ 69.5 − 3.7 + 3.5 pb at next-to-next-to-leading order in perturbative quantum chromodynamics.

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.

An analysis is presented based on models of the intrinsic transverse momentum (intrinsic k T ) of partons in nucleons by studying the dilepton transverse momentum in Drell-Yan events. Using parameter tuning in event generators and existing data from fixed-target experiments and from hadron colliders, our investigation spans 3 orders of magnitude in center-of-mass energy and 2 orders of magnitude in dilepton invariant mass. The results show an energy-scaling behavior of the intrinsic k T parameters, independent of the dilepton invariant mass at a given center-of-mass energy. © 2025 CERN, for the CMS Collaboration 2025 CERN

A search is presented for fractionally charged particles with charges below 1 e , using their small energy loss in the tracking detector as a key variable to observe a signal. The analyzed dataset corresponds to an integrated luminosity of 138 fb − 1 of proton-proton collisions collected at s = 13 TeV in 2016–2018 at the CERN LHC. This is the first search at the LHC for new particles with a charge between e / 3 and 0.9 e , including an extension of previous results at a charge of 2 e / 3 . Masses up to 640 GeV and charges as low as e / 3 are excluded at 95% confidence level. These are the most stringent limits to date for the considered Drell-Yan-like production mode. © 2025 CERN, for the CMS Collaboration 2025 CERN