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

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

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

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

Measurement of the double-differential inclusive jet cross section in proton-proton collisions at s\sqrt{s} = 5.02 TeV

International audienceThe inclusive jet cross section is measured as a function of jet transverse momentum $p_\mathrm{T}$ and rapidity $y$. The measurement is performed using proton-proton collision data at $\sqrt{s}$ = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb$^{-1}$. The jets are reconstructed with the anti-$k_\mathrm{T}$ algorithm using a distance parameter of $R$ = 0.4, within the rapidity interval $\lvert y\rvert$$\lt$ 2, and across the kinematic range 0.06 $\lt$$p_\mathrm{T}$$\lt$ 1 TeV. The jet cross section is unfolded from detector to particle level using the determined jet response and resolution. The results are compared to predictions of perturbative quantum chromodynamics, calculated at both next-to-leading order and next-to-next-to-leading order. The predictions are corrected for nonperturbative effects, and presented for a variety of parton distribution functions and choices of the renormalization/factorization scales and the strong coupling $\alpha_\mathrm{S}$

Measurement of the double-differential inclusive jet cross section in proton-proton collisions at s\sqrt{s} = 5.02 TeV

International audienceThe inclusive jet cross section is measured as a function of jet transverse momentum $p_\mathrm{T}$ and rapidity $y$. The measurement is performed using proton-proton collision data at $\sqrt{s}$ = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb$^{-1}$. The jets are reconstructed with the anti-$k_\mathrm{T}$ algorithm using a distance parameter of $R$ = 0.4, within the rapidity interval $\lvert y\rvert$$\lt$ 2, and across the kinematic range 0.06 $\lt$$p_\mathrm{T}$$\lt$ 1 TeV. The jet cross section is unfolded from detector to particle level using the determined jet response and resolution. The results are compared to predictions of perturbative quantum chromodynamics, calculated at both next-to-leading order and next-to-next-to-leading order. The predictions are corrected for nonperturbative effects, and presented for a variety of parton distribution functions and choices of the renormalization/factorization scales and the strong coupling $\alpha_\mathrm{S}$

Measurement of the double-differential inclusive jet cross section in proton-proton collisions at s\sqrt{s} = 5.02 TeV

International audienceThe inclusive jet cross section is measured as a function of jet transverse momentum $p_\mathrm{T}$ and rapidity $y$. The measurement is performed using proton-proton collision data at $\sqrt{s}$ = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb$^{-1}$. The jets are reconstructed with the anti-$k_\mathrm{T}$ algorithm using a distance parameter of $R$ = 0.4, within the rapidity interval $\lvert y\rvert$$\lt$ 2, and across the kinematic range 0.06 $\lt$$p_\mathrm{T}$$\lt$ 1 TeV. The jet cross section is unfolded from detector to particle level using the determined jet response and resolution. The results are compared to predictions of perturbative quantum chromodynamics, calculated at both next-to-leading order and next-to-next-to-leading order. The predictions are corrected for nonperturbative effects, and presented for a variety of parton distribution functions and choices of the renormalization/factorization scales and the strong coupling $\alpha_\mathrm{S}$

Measurement of the double-differential inclusive jet cross section in proton-proton collisions at s\sqrt{s} = 5.02 TeV

International audienceThe inclusive jet cross section is measured as a function of jet transverse momentum $p_\mathrm{T}$ and rapidity $y$. The measurement is performed using proton-proton collision data at $\sqrt{s}$ = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb$^{-1}$. The jets are reconstructed with the anti-$k_\mathrm{T}$ algorithm using a distance parameter of $R$ = 0.4, within the rapidity interval $\lvert y\rvert$$\lt$ 2, and across the kinematic range 0.06 $\lt$$p_\mathrm{T}$$\lt$ 1 TeV. The jet cross section is unfolded from detector to particle level using the determined jet response and resolution. The results are compared to predictions of perturbative quantum chromodynamics, calculated at both next-to-leading order and next-to-next-to-leading order. The predictions are corrected for nonperturbative effects, and presented for a variety of parton distribution functions and choices of the renormalization/factorization scales and the strong coupling $\alpha_\mathrm{S}$