Observation of γγ → ττ in proton-proton collisions and limits on the anomalous electromagnetic moments of the τ lepton

The production of a pair of τ leptons via photon–photon fusion, γγ → ττ, is observed for the f irst time in proton–proton collisions, with a significance of 5.3 standard deviations. This observation is based on a data set recorded with the CMS detector at the LHC at a center-of-mass energy of 13 TeV and corresponding to an integrated luminosity of 138 fb−1. Events with a pair of τ leptons produced via photon–photon fusion are selected by requiring them to be back-to-back in the azimuthal direction and to have a minimum number of charged hadrons associated with their production vertex. The τ leptons are reconstructed in their leptonic and hadronic decay modes. The measured fiducial cross section of γγ → ττ is σfid obs = 12.4+3.8 −3.1 fb. Constraints are set on the contributions to the anomalous magnetic moment (aτ) and electric dipole moments (dτ) of the τ lepton originating from potential effects of new physics on the γττ vertex: aτ = 0.0009+0.0032 −0.0031 and |dτ| < 2.9×10−17ecm (95% confidence level), consistent with the standard model

Search for Standard Model Production of Four Top Quarks in Compact Muon Solenoid Detector and Designing Neural Networks for Top Quark Yukawa Coupling Measurement from Four Top Quark Production

The production of four top quarks has been predicted by the Standard Model (SM) and has been considered as one of its important tests. The measurements obtainable from this production, such as its cross section, can be compared with predictions provided by SM. This comparison can determine whether SM is adequate to explain this phenomenon. The four-top quark production is also sensitive to the coupling strength between the top quark and the Higgs boson, also known as the top Yukawa coupling. By considering Feynman diagrams for this production, this coupling value affects both its cross section and the kinematics of the top quarks within the production. These changes caused by top Yukawa coupling offer potential new information for novel techniques, such as neural networks, to measure the coupling value. This dissertation describes the combination of four-top quark production searches in different final states, along with a study of the use of neural networks for the measurement of top quark Yukawa coupling

Search for Standard Model Production of Four Top Quarks in Compact Muon Solenoid Detector and Designing Neural Networks for Top Quark Yukawa Coupling Measurement from Four Top Quark Production

The production of four top quarks has been predicted by the Standard Model(SM) and has been considered as one of its important tests. The measurementsobtainable from this production, such as its cross section, can be comparedwith predictions provided by SM. This comparison can determine whether SMis adequate to explain this phenomenon. The four-top quark production is alsosensitive to the coupling strength between the top quark and the Higgs boson, alsoknown as the top Yukawa coupling. By considering Feynman diagrams for thisproduction, this coupling value affects both its cross section and the kinematics ofthe top quarks within the production. These changes caused by top Yukawa couplingoffer potential new information for novel techniques, such as neural networks, tomeasure the coupling value. This dissertation describes the combination of four-topquark production searches in different final states, along with a study of the use ofneural networks for the measurement of top quark Yukawa coupling

Search for four-top-quark production with four-lepton final states in CMS

The search for four-top-quark production with four leptons as a final product at \sqrt{s} = 13 \mbox{ TeV} is presented in this report. In this analysis, Monte-Carlo generated datasets, both with four-top-quark production process and background processes, are used to train two machine learning systems with boosted decision tree and neural networks. By utilising discriminators from these two machine learning techniques, we have determined limits of four-top-quark production cross section in \mbox{35.9 fb}^{-1} of CMS data recorded in 2016, approaching the sensitivity of cross section limits reported in previous analyses by the CMS experiment

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

Application of Adversarial Networks in search for four top quark production in CMS

One burden of high energy physics data analysis is uncertainty within the measurement, both systematically and statistically. Even with sophisticated neural network techniques that are used to assist in high energy physics measurements, the resulting measurement may suffer from both types of uncertainties. Fortunately, most types of systematic uncertainties are based on knowledge from information such as theoretical assumptions, for which the range and behaviour are known. It has been proposed to mitigate such systematic uncertainties by using a new type of neural network called adversarial neural network (ANN) that would make the discriminator less sensitive to these uncertainties, but this has not yet been demonstrated in a real LHC analysis. This work investigates ANNs using as a benchmark the search for the production of four top quarks, an extremely rare physics process at the LHC and one of the important processes that can prove or disprove the Standard Model. The search for four top quarks in some cases is sensitive to large systematic uncertainties. Discriminators based on traditional and adversarial neural networks are trained and chosen via hyperparameter adjustment. The expected cross section upper limit and expected significance for four top quark production is calculated using traditional neural networks and adversarial neural networks based on simulated proton-proton collisions within the Compact Muon Solenoid detector within Large Hadron Collider, and are compared to existing results. The improvement and further considerations to the search for rare processes at the LHC will be discussed

Autoencoder-Based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter

The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online data quality monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. In addition, the first results from deploying the autoencoder-based system in the CMS online data quality monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system

Proton reconstruction with the TOTEM Roman pot detectors for high-β∗\beta^* LHC data

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 $\mu$m 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 $\mu$m in the horizontal and 60 $\mu$m in the vertical directions. The goal is to provide an optimal reconstruction tool for central exclusive physics analyses based on the high-$\beta^*$ data-taking period at $\sqrt{s}$ = 13 TeV in 2018