Observation du boson de Higgs et mesure de ses propriétés dans le canal HWW*avec le détecteur ATLAS au LHC

The Higgs boson decays to WW* is observed based on an excess of events over background of 6.1 standard deviations in the dilepton final state including all flavour channels (ee/μμ and eμ/μe) and jet multiplicity categories (nj = 0, 1,≥ 2), where theStandard Model expectation is 5.8 standard deviations. Evidence for thevector-boson fusion (VBF) production process is also obtained with a significance of3.2 standard deviations. The different favour channels eμ/μe have the best expected signal sensitivity. The low jet multiplicity categories are dominantly contributed by the gluon-gluon fusion (ggF) production process whereas the large jet multiplicity category has the best sensitivity to the VBF production process. The results are obtained from proton-proton collision data recorded by the ATLAS detector at the LHC, corresponding to 4.5fb-1 at √s = 7TeV and 20.3fb-1 at √s = 8TeV. The background contribution in each channel and jet multiplicity category varies and is determined mostly with data-driven techniques with dedicated control or validation regions. The dominant background processes are the continuum WW and top quark productions. In the same flavour channels (ee/μμ), the Drell-Yan process is another important background source. For a Higgs boson mass of 125.36GeV, the ratio of the measured value to the expected value of the total production cross section times branching ratio fraction is 1.09+0.16-0.15 (stat.) +0.17-0.14 (syst.). The corresponding ratios for the gluon fusion and vector-boson fusion production modes are 1.02 ± 0.19 (stat.)+0.22−0.18 (syst.) and 1.27+0.44-0.40 (stat.) +0.30-0.21 (syst.), respectively. At √s = 8TeV, the total production cross section is measured to beσ(gg → H → WW∗) = 4.6±0.9 (stat.)+0.8−0.7 (syst.) pb σ(VBFH → WW∗) = 0.51+0.17−0.15 (stat.)+0.13−0.08 (syst.) pb. The fiducial cross section is determined for the gluon-fusion process in exclusive final states with zero and one associated jet. In addition to the on-shell couplings, other properties of the Higgs boson, namely the spin quantum number and the total decay width, are also studied using the 8TeV data and the different flavour channels only. The spin study is based on the on-shell dominated event sample using the nj ≤ 1 jet categories. The Standard Model spin-parity JCP = 0++ hypothesis is compared with alternative hypotheses. The data are found to be consistent with the Standard Model and limits are placed on alternative spin hypotheses.The off-shell events in the high mass tail from the inclusive jet category are then used to measure the off-shell couplings and impose a constraint on the upper limit of the total width of the Higgs boson indirectly, when certain assumptions are made.Le boson de Higgs dans le mode de désintégration WW* est observé avec un excès d'événements sur le bruit de fond de 6,1 écarts-types dans l'état final avec dilepton, alors que l’importance du signal attendu pour le boson de Higgs du modèle standard est de 5,8 écarts-types. Une indication pour la production du processus en fusion de bosons vecteurs (VBF) est également obtenue avec une importance de 3,2 écarts-types. Les résultats sont obtenus à partir d'un échantillon de données en collisions proton-proton enregistrées par le détecteur ATLAS au LHC, qui correspond à une luminosité intégrée de 4,5fb-1 à √s = 7 TeV et 20,3fb-1 à √s = 8 TeV. Tous les canaux de saveur leptonique (ee/μμ et eμ/μe)) sont analysés, y compris de différentes catégories en multiplicité de jets (nj = 0, 1,≥ 2). Les canaux ayant différentes saveurs leptoniques eμ/μe ont la meilleure sensibilité au signal. Les catégories à basse multiplicité de jet sont contribuées principalement par la production du processus en fusion de gluon-gluon (ggF), tandis que la catégorie à haute multiplicité est plus sensible à la production VBF. Les bruits de fond dans différents canaux et catégories varient et leurs contributions sont obtenues dans la plupart de cas à partir des données avec des régions de contrôle ou validation. Les bruits de fond dominants sont les productions WW et le quark top. Dans les canaux ayant la même saveur leptonique, la contribution Drell-Yan est aussi une autre source importante. Pour le boson de Higgs à 125,36GeV, le rapport du signal mesuré sur celui du modèle standard est de 1,09+0,16-0,15 (stat.) +0,17-0,14 (syst.). Les rapports correspondants pour les productions ggF et VBF sont de 1,02±0, 19 (stat.) +0,22-0,18 (syst.) et 1,27+0,44-0,40 (stat.) +0,30-0,21 (syst.), respectivement. La section efficace totale mesurée à √s = 8 TeV est de σ(gg → H → WW∗) =8TeV est de σ(gg → H → WW∗) et σ(VBFH → WW∗) = 0,51+0,17−0,15 (stat.)+0,13−0,08 (syst.) pb. La section efficace fiducielle est aussi mesurée pour la production ggF dans l'était final exclusif avec zéro ou un seul jet. En plus des couplages, d'autres propriétés du boson de Higgs, notamment le nombre quantique de spin et la largeur totale de désintégration, sont également étudiées en utilisant les données de 8TeV et les canaux ayant différentes saveurs leptoniques seulement. L'étude du spin est basée sur un échantillon de données dominé par les événements sous le pic de résonance en utilisant les catégories de jet nj ≤ 1. L'hypothèse sur le spin-parité JCP = 0++ du modèle standard est comparée à d'autres hypothèses. Les données sont compatibles avec le modèle standard et les limites sont placées sur des hypothèses alternatives de spin. Les événements dans la queue hors pic de résonance à haute masse dans la catégorie inclusive de jet sont ensuite utilisés pour mesurer les couplages du boson de Higgs hors de sa couche de masse et pour imposer une contrainte sur la limite supérieure de la largeur totale du boson de Higgs, lorsque certaines hypothèses sont faite

Merger and Acquisition: the Effect of Financial Constraint and Security Analysts on Bidder Abnormal Return

This thesis investigates to what extent financial constraint and financial disparity influence bidder merger performance, how analyst recommendation consensus relates to bidder announcement return, and whether divergence opinion and information asymmetry affect M&A abnormal returns. First, this thesis examines the impact of financial constraint and the financial constraint disparity between bidder and target on bidder abnormal return. I find that a constrained acquirer outperforms an unconstrained bidder in both the long and short run; target financial constraint is significantly negatively related to bidder announcement return. Acquiring a financially constrained target tends to positively influence an acquirer’s abnormal returns in the long run. In addition, disparity between acquirer target financial constraints (ATDKZ) is negatively related to bid premium. Second, this paper investigates whether analyst recommendations affect merger and acquisition performance: whether recommendation consensus has the predicting power on acquisition performance, and if so, which type of recommendation consensus is more accurate than the others. The results suggest that recommendation consensus is positively related to acquirers’ announcement return; acquirers with high recommendation consensus before announcement day outperform acquirers with low recommendation consensus in the short run; analysts can successfully predict the incoming M&A deals and adjust their recommendation accordingly; and the recommendation consensus estimated 90 days preceding deal announcement has the strongest predicting power. It suggests that analysts do have the superior skill. Finally, this study estimates how the combination of analyst divergence opinion and information asymmetry influences bidder abnormal return by controlling bidder pre-merger performance. A low divergence opinion bidder outperforms a high divergence opinion bidder in both the long and short run. This effect is much stronger in the sample of poorly performed bidders than well-performed bidders. For bidders with poor pre-merger performance, analyst divergence opinion has negative impact on announcement return. For bidders with good pre-merger performance, a positive relation has been found between information asymmetry and announcement return. These empirical results strongly support that bidder pre-merger performance is an important conditioning variable that we should take into consideration in examining the impact of divergence opinion and information asymmetry on bidder merger and acquisition performance. Overall, this thesis provides new empirical evidence on how bidder M&A performance is related to financial constraint, financial constraint disparity, recommendation consensus, divergence opinion and information asymmetry. The results suggest that constrained bidders outperform unconstrained bidders, financial analyst do have superior skills, and pre-merger performance is an important controlling variable when we study divergence opinion and information asymmetry in the context of M&A abnormal return

Learning Preconditioner for Conjugate Gradient PDE Solvers

Efficient numerical solvers for partial differential equations empower science and engineering. One of the commonly employed numerical solvers is the preconditioned conjugate gradient (PCG) algorithm which can solve large systems to a given precision level. One challenge in PCG solvers is the selection of preconditioners, as different problem-dependent systems can benefit from different preconditioners. We present a new method to introduce \emph{inductive bias} in preconditioning conjugate gradient algorithm. Given a system matrix and a set of solution vectors arise from an underlying distribution, we train a graph neural network to obtain an approximate decomposition to the system matrix to be used as a preconditioner in the context of PCG solvers. We conduct extensive experiments to demonstrate the efficacy and generalizability of our proposed approach in solving various 2D and 3D linear second-order PDEs

Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks

Deep learning is known to be data-hungry, which hinders its application in many areas of science when datasets are small. Here, we propose to use transfer learning methods to migrate knowledge between different physical scenarios and significantly improve the prediction accuracy of artificial neural networks trained on a small dataset. This method can help reduce the demand for expensive data by making use of additional inexpensive data. First, we demonstrate that in predicting the transmission from multilayer photonic film, the relative error rate is reduced by 46.8% (26.5%) when the source data comes from 10-layer (8-layer) films and the target data comes from 8-layer (10-layer) films. Second, we show that the relative error rate is decreased by 22% when knowledge is transferred between two very different physical scenarios: transmission from multilayer films and scattering from multilayer nanoparticles. Finally, we propose a multi-task learning method to improve the performance of different physical scenarios simultaneously in which each task only has a small dataset

Slip and Jump Coefficients for General Gas-Surface Interactions According to the Moment Method

We develop a moment method based on the Hermite series of arbitrary order to calculate viscous-slip, thermal-slip, and temperature-jump coefficients for general gas-surface scattering kernels. Under some usual assumptions of scattering kernels, the solvability is obtained by showing the positive definiteness of the symmetric coefficient matrix in the boundary conditions. For gas flows with the Cercignani-Lampis gas-surface interaction and inverse-power-law intermolecular potentials, the model can capture the slip and jump coefficients accurately with elegant analytic expressions. On the one hand, the proposed method can apply to the cases of arbitrary order moments with increasing accuracy. On the other hand, the explicit formulae for low-order situations are simpler and more accurate than some existing results in references. Therefore, one may apply these formulae in slip and jump conditions to improve the accuracy of macroscopic fluid dynamic models for gas flows