Xavier Coubez

The charge asymmetry in the production of top quark and antiquark pairs is measured in proton-proton collisions at a center-of-mass energy of 8 TeV. The data, corresponding to an integrated luminosity of 19.6 inverse femtobarns, were collected by the CMS experiment at the LHC. Events with a single isolated electron or muon, and four or more jets, at least one of which is likely to have originated from hadronization of a bottom quark, are selected. A template technique is used to measure the asymmetry in the distribution of differences in the top quark and antiquark absolute rapidities. The measured asymmetry is A[c,y] = [0.33 +/- 0.26 (stat) +/- 0.33 (syst)]%, which is the most precise result to date. The results are compared to calculations based on the standard model and on several beyond-the-standard-model scenarios.

Modelling irregularly-sampled time series (ISTS) is challenging because of missing values. Most existing methods focus on handling ISTS by converting irregularly sampled data into regularly sampled data via imputation. These models assume an underlying missing mechanism leading to unwanted bias and sub-optimal performance. We present SLAN (Switch LSTM Aggregate Network), which utilizes a pack of LSTMs to model ISTS without imputation, eliminating the assumption of any underlying process. It dynamically adapts its architecture on the fly based on the measured sensors. SLAN exploits the irregularity information to capture each sensor's local summary explicitly and maintains a global summary state throughout the observational period. We demonstrate the efficacy of SLAN on publicly available datasets, namely, MIMIC-III, Physionet 2012 and Physionet 2019. The code is available at https://github.com/Rohit102497/SLAN.

A search for massive resonances decaying to a Z boson and a photon is performed in events with a hadronically decaying Z boson candidate, separately in light-quark and b quark decay modes, identified using jet substructure and advanced b tagging techniques. Results are based on samples of proton-proton collisions collected with the CMS detector at the LHC at center-of-mass energies of 8 and 13 TeV, corresponding to integrated luminosities of 19.7 and 2.7 inverse femtobarns, respectively. The results of the search are combined with those of a similar search in the leptonic decay modes of the Z boson, based on the same data sets. Spin-0 resonances with various widths and with masses in a range between 0.2 and 3.0 TeV are considered. No significant excess is observed either in the individual analyses or the combination. The results are presented in terms of upper limits on the production cross section of such resonances and constitute the most stringent limits to date for a wide range of masses.

The inelastic hadronic cross section in proton-lead collisions at a centre-of-mass energy per nucleon pair of 5.02 TeV is measured with the CMS detector at the LHC. The data sample, corresponding to an integrated luminosity of 12.6 +/- 0.4 inverse nanobarns, has been collected with an unbiased trigger for inclusive particle production. The cross section is obtained from the measured number of proton-lead collisions with hadronic activity produced in the pseudorapidity ranges 3<abs(eta)<5 and/or -5<abs(eta)<-3, corrected for photon-induced contributions, experimental acceptance, and other instrumental effects. The inelastic cross section is measured to be sigma[inel,pPb]=2061 +/- 3 (stat) +/- 34 (syst) +/- 72 (lum) mb. Various Monte Carlo generators, commonly used in heavy ion and cosmic ray physics, are found to reproduce the data within uncertainties. The value of sigma[inel,pPb] is compatible with that expected from the proton-proton cross section at 5.02 TeV scaled up within a simple Glauber approach to account for multiple scatterings in the lead nucleus, indicating that further net nuclear corrections are small.

The discovery in 2012 of the last elementary particle predicted by the Standard Model, the Higgs boson, has opened a new era in particle physics. One of the objectives now is to probe the coupling of the Higgs boson to other particles in order to confirm the validity of the model. The work of this thesis focused initially on the identification of jets coming from b quark at trigger level. The goal is to allow for the selection of one thousand events among the forty million produced every second at the LHC, by identifiying objects present in the final states of interesting physics processes such as the associated production of a Higgs boson decaying in a pair of b quark with a Z boson decaying into undetected neutrinos. The work then moved to the study of the coupling of the Higgs boson to the quark top, most massive particle in the Standard Model. After a study of one of the important background of the associated production of the Higgs boson and a top quark pair, a new method called matrix element method has been used to improve the discrimination between signal and background. This analysis has led to the first experimental evidence of coupling between the Higgs boson and the top quark.

A measurement of the ttbar production cross section at sqrt(s)=13 TeV is presented using proton-proton collisions, corresponding to an integrated luminosity of 2.3 inverse femtobarns, collected with the CMS detector at the LHC. Final states with one isolated charged lepton (electron or muon) and at least one jet are selected and categorized according to the accompanying jet multiplicity. From a likelihood fit to the invariant mass distribution of the isolated lepton and a jet identified as coming from the hadronization of a bottom quark, the cross section is measured to be sigma(ttbar)= 835 +/- 3 (stat) +/- 23 (syst) +/- 23 (lum) pb, in agreement with the standard model prediction. Using the expected dependence of the cross section on the pole mass of the top quark (m[t]), the value of m[t] is found to be 172.7+2.4-2.7 GeV.

Deep learning is a standard tool in the field of high-energy physics, facilitating considerable sensitivity enhancements for numerous analysis strategies. In particular, in identification of physics objects, such as jet flavor tagging, complex neural network architectures play a major role. However, these methods are reliant on accurate simulations. Mismodeling can lead to non-negligible differences in performance in data that need to be measured and calibrated against. We investigate the classifier response to input data with injected mismodelings and probe the vulnerability of flavor tagging algorithms via application of adversarial attacks. Subsequently, we present an adversarial training strategy that mitigates the impact of such simulated attacks and improves the classifier robustness. We examine the relationship between performance and vulnerability and show that this method constitutes a promising approach to reduce the vulnerability to poor modeling.

Deep learning is a standard tool in the field of high-energy physics, facilitating considerable sensitivity enhancements for numerous analysis strategies. In particular, in identification of physics objects, such as jet flavor tagging, complex neural network architectures play a major role. However, these methods are reliant on accurate simulations. Mismodeling can lead to non-negligible differences in performance in data that need to be measured and calibrated against. We investigate the classifier response to input data with injected mismodelings and probe the vulnerability of flavor tagging algorithms via application of adversarial attacks. Subsequently, we present an adversarial training strategy that mitigates the impact of such simulated attacks and improves the classifier robustness. We examine the relationship between performance and vulnerability and show that this method constitutes a promising approach to reduce the vulnerability to poor modeling.