Interpretations and Implications of the Top Quark Rapidity Asymmetries AFBtA_{FB}^t and AFBℓA_{FB}^{\ell}

Forward-backward asymmetries $A_{FB}^t$ and $A_{FB}^\ell$ are observed in the top quark $t$ rapidity distribution and in the rapidity distribution of charged leptons $\ell$ from top quark decay at the Tevatron proton-antiproton collider, and a charge asymmetry $A_C$ is seen in proton-proton collisions at the Large Hadron Collider (LHC). In this paper, we update our previous studies of the Tevatron asymmetries using the most recent data. We provide expectations for $A_C$ at the LHC based first on model independent extrapolations from the Tevatron, and second based on new physics models that can explain the Tevatron asymmetries. We examine the relationship of the two asymmetries $A_{FB}^t$ and $A_{FB}^\ell$. We show their connection through the $(V-A)$ spin correlation between the charged lepton and the top quark with different polarization states. We show that the ratio of the two asymmetries provides independent insight into new physics models that are invoked to fit the top quark asymmetry. We emphasize the value of the measurement of both asymmetries, and we conclude that a model which produces more right-handed than left-handed top quarks is favored by the present Tevatron data.Comment: Some figures changed. A typo in appendix fixed. Published in Physical Review

Top Quark Polarization As A Probe of Models with Extra Gauge Bosons

New heavy gauge bosons exist in many models of new physics beyond the standard model of particle physics. Discovery of these W^\prime and Z^\prime resonances and the establishment of their spins, couplings, and other quantum numbers would shed light on the gauge structure of the new physics. The measurement of the polarization of the SM fermions from the gauge boson decays would decipher the handedness of the coupling of the new states, an important relic of the primordial new physics symmetry. Since the top quark decays promptly, its decay preserves spin information. We show how decays of new gauge bosons into third generation fermions (W^\prime \to tb, Z^\prime\to t\bar{t}) can be used to determine the handedness of the couplings of the new states and to discriminate among various new physics models

Top Quark Forward-Backward Asymmetry and Same-Sign Top Quark Pairs

The top quark forward-backward asymmetry measured at the Tevatron collider shows a large deviation from standard model expectations. Among possible interpretations, a non-universal $Z^\prime$ model is of particular interest as it naturally predicts a top quark in the forward region of large rapidity. To reproduce the size of the asymmetry, the couplings of the $Z^\prime$ to standard model quarks must be large, inevitably leading to copious production of same-sign top quark pairs at the energies of the Large Hadron Collider (LHC). We explore the discovery potential for $tt$ and $ttj$ production in early LHC experiments at 7-8 TeV and conclude that if {\it no} $tt$ signal is observed with 1 fb$^{-1}$ of integrated luminosity, then a non-universal $Z^\prime$ alone cannot explain the Tevatron forward-backward asymmetry.Comment: Tevatron limit from same-sign tt search adde

The Top Quark Production Asymmetries AFBtA_{FB}^t and AFBℓA_{FB}^{\ell}

A large forward-backward asymmetry is seen in both the top quark rapidity distribution $A_{FB}^t$ and in the rapidity distribution of charged leptons $A_{FB}^\ell$ from top quarks produced at the Tevatron. We study the kinematic and dynamic aspects of the relationship of the two observables arising from the spin correlation between the charged lepton and the top quark with different polarization states. We emphasize the value of both measurements, and we conclude that a new physics model which produces more right-handed than left-handed top quarks is favored by the present data.Comment: accepted for publication in Physical Review Letter

A graph-cut approach to image segmentation using an affinity graph based on l0−sparse representation of features

International audienceWe propose a graph-cut based image segmentation method by constructing an affinity graph using l0 sparse representation. Computing first oversegmented images, we associate with all segments, that we call superpixels, a collection of features. We find the sparse representation of each set of features over the dictionary of all features by solving a l0-minimization problem. Then, the connection information between superpixels is encoded as the non-zero representation coefficients, and the affinity of connected superpixels is derived by the corresponding representation error. This provides a l0 affinity graph that has interesting properties of long range and sparsity, and a suitable graph cut yields a segmentation. Experimental results on the BSD database demonstrate that our method provides perfectly semantic regions even with a constant segmentation number, but also that very competitive quantitative results are achieved

Color Sextet Vector Bosons and Same-Sign Top Quark Pairs at the LHC

We investigate the production of beyond-the-standard-model color-sextet vector bosons at the Large Hadron Collider and their decay into a pair of same-sign top quarks. We demonstrate that the energy of the charged lepton from the top quark semi-leptonic decay serves as a good measure of the top-quark polarization, which, in turn determines the quantum numbers of the boson and distinguishes vector bosons from scalars

Effective and efficient visual description based on local binary patterns and gradient distribution for object recognition

Cette thèse est consacrée au problème de la reconnaissance visuelle des objets basé sur l'ordinateur, qui est devenue un sujet de recherche très populaire et important ces dernières années grâce à ses nombreuses applications comme l'indexation et la recherche d'image et de vidéo , le contrôle d'accès de sécurité, la surveillance vidéo, etc. Malgré beaucoup d'efforts et de progrès qui ont été fait pendant les dernières années, il reste un problème ouvert et est encore considéré comme l'un des problèmes les plus difficiles dans la communauté de vision par ordinateur, principalement en raison des similarités entre les classes et des variations intra-classe comme occlusion, clutter de fond, les changements de point de vue, pose, l'échelle et l'éclairage. Les approches populaires d'aujourd'hui pour la reconnaissance des objets sont basé sur les descripteurs et les classiffieurs, ce qui généralement extrait des descripteurs visuelles dans les images et les vidéos d'abord, et puis effectue la classification en utilisant des algorithmes d'apprentissage automatique sur la base des caractéristiques extraites. Ainsi, il est important de concevoir une bonne description visuelle, qui devrait être à la fois discriminatoire et efficace à calcul, tout en possédant certaines propriétés de robustesse contre les variations mentionnées précédemment. Dans ce contexte, l objectif de cette thèse est de proposer des contributions novatrices pour la tâche de la reconnaissance visuelle des objets, en particulier de présenter plusieurs nouveaux descripteurs visuelles qui représentent effectivement et efficacement le contenu visuel d image et de vidéo pour la reconnaissance des objets. Les descripteurs proposés ont l'intention de capturer l'information visuelle sous aspects différents. Tout d'abord, nous proposons six caractéristiques LBP couleurs de multi-échelle pour traiter les défauts principaux du LBP original, c'est-à-dire, le déffcit d'information de couleur et la sensibilité aux variations des conditions d'éclairage non-monotoniques. En étendant le LBP original à la forme de multi-échelle dans les différents espaces de couleur, les caractéristiques proposées non seulement ont plus de puissance discriminante par l'obtention de plus d'information locale, mais possèdent également certaines propriétés d'invariance aux différentes variations des conditions d éclairage. En plus, leurs performances sont encore améliorées en appliquant une stratégie de l'image division grossière à fine pour calculer les caractéristiques proposées dans les blocs d'image afin de coder l'information spatiale des structures de texture. Les caractéristiques proposées capturent la distribution mondiale de l information de texture dans les images. Deuxièmement, nous proposons une nouvelle méthode pour réduire la dimensionnalité du LBP appelée la combinaison orthogonale de LBP (OC-LBP). Elle est adoptée pour construire un nouveau descripteur local basé sur la distribution en suivant une manière similaire à SIFT. Notre objectif est de construire un descripteur local plus efficace en remplaçant l'information de gradient coûteux par des patterns de texture locales dans le régime du SIFT. Comme l'extension de notre première contribution, nous étendons également le descripteur OC-LBP aux différents espaces de couleur et proposons six descripteurs OC-LBP couleurs pour améliorer la puissance discriminante et la propriété d'invariance photométrique du descripteur basé sur l'intensité. Les descripteurs proposés capturent la distribution locale de l information de texture dans les images. Troisièmement, nous introduisons DAISY, un nouveau descripteur local rapide basé sur la distribution de gradient, dans le domaine de la reconnaissance visuelle des objets. [...]This thesis is dedicated to the problem of machine-based visual object recognition, which has become a very popular and important research topic in recent years because of its wide range of applications such as image/video indexing and retrieval, security access control, video monitoring, etc. Despite a lot of e orts and progress that have been made during the past years, it remains an open problem and is still considered as one of the most challenging problems in computer vision community, mainly due to inter-class similarities and intra-class variations like occlusion, background clutter, changes in viewpoint, pose, scale and illumination. The popular approaches for object recognition nowadays are feature & classifier based, which typically extract visual features from images/videos at first, and then perform the classification using certain machine learning algorithms based on the extracted features. Thus it is important to design good visual description, which should be both discriminative and computationally efficient, while possessing some properties of robustness against the previously mentioned variations. In this context, the objective of this thesis is to propose some innovative contributions for the task of visual object recognition, in particular to present several new visual features / descriptors which effectively and efficiently represent the visual content of images/videos for object recognition. The proposed features / descriptors intend to capture the visual information from different aspects. Firstly, we propose six multi-scale color local binary pattern (LBP) features to deal with the main shortcomings of the original LBP, namely deficiency of color information and sensitivity to non-monotonic lighting condition changes. By extending the original LBP to multi-scale form in different color spaces, the proposed features not only have more discriminative power by obtaining more local information, but also possess certain invariance properties to different lighting condition changes. In addition, their performances are further improved by applying a coarse-to-fine image division strategy for calculating the proposed features within image blocks in order to encode spatial information of texture structures. The proposed features capture global distribution of texture information in images. Secondly, we propose a new dimensionality reduction method for LBP called the orthogonal combination of local binary patterns (OC-LBP), and adopt it to construct a new distribution-based local descriptor by following a way similar to SIFT.Our goal is to build a more efficient local descriptor by replacing the costly gradient information with local texture patterns in the SIFT scheme. As the extension of our first contribution, we also extend the OC-LBP descriptor to different color spaces and propose six color OC-LBP descriptors to enhance the discriminative power and the photometric invariance property of the intensity-based descriptor. The proposed descriptors capture local distribution of texture information in images. Thirdly, we introduce DAISY, a new fast local descriptor based on gradient distribution, to the domain of visual object recognition.LYON-Ecole Centrale (690812301) / SudocSudocFranceF