Constraining multi-Higgs flavour models

To study a flavour model with a non-minimal Higgs sector one must first define the symmetries of the fields; then identify what types of vacua exist and how they may break the symmetries; and finally determine whether the remnant symmetries are compatible with the experimental data. Here we address all these issues in the context of flavour models with any number of Higgs doublets. We stress the importance of analysing the Higgs vacuum expectation values that are pseudo-invariant under the generators of all subgroups. It is shown that the only way of obtaining a physical CKM mixing matrix and, simultaneously, non-degenerate and non-zero quark masses is requiring the vacuum expectation values of the Higgs fields to break completely the full flavour group, except possibly for some symmetry belonging to baryon number. The application of this technique to some illustrative examples, such as the flavour groups Delta (27), A(4) and S-3, is also presented.

New textures for the lepton mass matrices

We study predictive textures for the lepton mass matrices in which the charged-lepton mass matrix has either four or five zero matrix elements while the neutrino Majorana mass matrix has, respectively, either four or three zero matrix elements. We find that all the viable textures of these two kinds share many predictions: the neutrino mass spectrum is inverted, the sum of the light-neutrino masses is close to 0.1 eV, the Dirac phase delta in the lepton mixing matrix is close to either 0 or pi, and the mass term responsible for neutrinoless double-beta decay lies in between 12 and 22 meV. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Blind hyperspectral unmixing

This paper introduces a new hyperspectral unmixing method called Dependent Component Analysis (DECA). This method decomposes a hyperspectral image into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA models the abundance fractions as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. DECA performance is illustrated using simulated and real data.

Hyperspectral unmixing algorithm via dependent component analysis

This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abudances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

New CP violation in neutrino oscillations

Measurements of CP-violating observables in neutrino oscillation experiments have been studied in the literature as a way to determine the CP-violating phase in the mixing matrix for leptons. Here we show that such observables also probe new neutrino interactions in the production or detection processes. Genuine CP violation and fake CP violation due to matter effects are sensitive to the imaginary and real parts of new couplings. The dependence of the CP asymmetry on the source-detector distance is different from the standard one and, in particular, enhanced at short distances. We estimate that future neutrino factories will be able to probe in this way new interactions that are up to four orders of magnitude weaker than the weak interactions. We discuss the possible implications for models of new physics.

Contribution to the Development of the LHCb Vertex Locator Readout Electronics

L'expérience LHCb sera installée sur le futur accélérateur LHC du CERN. LHCb est un spectromètre à un bras consacré aux mesures de précision de la violation CP et à l'étude des désintégrations rares des particules qui contiennent un quark b. Actuellement LHCb se trouve dans la phase finale de recherche et développement et de conception. La construction a déjà commencé pour l'aimant et les calorimètres. Dans le Modèle Standard, la violation CP est causée par une phase complexe dans la matrice 3x3 CKM (Cabibbo-Kobayashi-Maskawa) de mélange des quarks. L'expérience LHCb compte utiliser les mesons B pour tester l'unitarité de cette matrice, en mesurant de diverses manières indépendantes tous les angles et côtés du "triangle d'unitarité". Cela permettra de surdéterminer le modèle et, peut-être, de mettre en évidence des incohérences qui seraient le signal de l'existence d'une physique au-delà du Modèle Standard. La reconstruction du vertex de désintégration des particules est une condition fondamentale pour l'expérience LHCb. La présence d'un vertex secondaire déplacé est une signature de la désintégration de particules avec un quark b. Cette signature est utilisée dans le trigger topologique du LHCb. Le Vertex Locator (VeLo) doit fournir des mesures précises de coordonnées de passage des traces près de la région d'interaction. Ces points sont ensuite utilisés pour reconstruire les trajectoires des particules et l'identification des vertices secondaires et la mesure des temps de vie des hadrons avec quark b. L'électronique du VeLo est une partie essentielle du système d'acquisition de données et doit se conformer aux spécifications de l'électronique de LHCb. La conception des circuits doit maximiser le rapport signal/bruit pour obtenir la meilleure performance de reconstruction des traces dans le détecteur. L'électronique, conçue en parallèle avec le développement du détecteur de silicium, a parcouru plusieurs phases de "prototyping" décrites dans cette thèse.<br/><br/>The LHCb experiment is being built at the future LHC accelerator at CERN. It is a forward single-arm spectrometer dedicated to precision measurements of CP violation and rare decays in the b quark sector. Presently it is finishing its R&D and final design stage. The construction already started for the magnet and calorimeters. In the Standard Model, CP violation arises via the complex phase of the 3 x 3 CKM (Cabibbo-Kobayashi-Maskawa) quark mixing matrix. The LHCb experiment will test the unitarity of this matrix by measuring in several theoretically unrelated ways all angles and sides of the so-called "unitary triangle". This will allow to over-constrain the model and - hopefully - to exhibit inconsistencies which will be a signal of physics beyond the Standard Model. The Vertex reconstruction is a fundamental requirement for the LHCb experiment. Displaced secondary vertices are a distinctive feature of b-hadron decays. This signature is used in the LHCb topology trigger. The Vertex Locator (VeLo) has to provide precise measurements of track coordinates close to the interaction region. These are used to reconstruct production and decay vertices of beauty-hadrons and to provide accurate measurements of their decay lifetimes. The Vertex Locator electronics is an essential part of the data acquisition system and must conform to the overall LHCb electronics specification. The design of the electronics must maximise the signal to noise ratio in order to achieve the best tracking reconstruction performance in the detector. The electronics is being designed in parallel with the silicon detector development and went trough several prototyping phases, which are described in this thesis.

Colligation and Cross Moment-Based Approaches for Independent Component Analysis

Diplomityössä on käsitelty uudenlaisia menetelmiä riippumattomien komponenttien analyysiin(ICA): Menetelmät perustuvat colligaatioon ja cross-momenttiin. Colligaatio menetelmä perustuu painojen colligaatioon. Menetelmässä on käytetty kahden tyyppisiä todennäköisyysjakaumia yhden sijasta joka perustuu yleiseen itsenäisyyden kriteeriin. Työssä on käytetty colligaatio lähestymistapaa kahdella asymptoottisella esityksellä. Gram-Charlie ja Edgeworth laajennuksia käytetty arvioimaan todennäköisyyksiä näissä menetelmissä. Työssä on myös käytetty cross-momentti menetelmää joka perustuu neljännen asteen cross-momenttiin. Menetelmä on hyvin samankaltainen FastICA algoritmin kanssa. Molempia menetelmiä on tarkasteltu lineaarisella kahden itsenäisen muuttajan sekoituksella. Lähtö signaalit ja sekoitetut matriisit ovattuntemattomia signaali lähteiden määrää lukuunottamatta. Työssä on vertailtu colligaatio menetelmään ja sen modifikaatioita FastICA:an ja JADE:en. Työssä on myös tehty vertailu analyysi suorituskyvyn ja keskusprosessori ajan suhteen cross-momenttiin perustuvien menetelmien, FastICA:n ja JADE:n useiden sekoitettujen parien kanssa.

Calculable lepton masses, seesaw relations, and four neutrino mixings in a 3-3-1 model with an extra U(1) symmetry

We propose a scheme in which the masses of the heavier leptons obey seesaw type relations. The light lepton masses, except the electron and the electron neutrino ones, are generated by one loop level radiative corrections. We work in a version of the 3-3-1 electroweak model that predicts singlets (charged and neutral) of heavy leptons beyond the known ones. An extra U(1)(Omega) symmetry is introduced in order to avoid the light leptons getting masses at the tree level. The electron mass induces an explicit symmetry breaking at U(1)(Omega). We discuss also the mixing matrix among four neutrinos. The new energy scale required is not higher than a few TeV.

Lepton masses from a TeV scale in a 3-3-1 model

In this work, using the fact that in 3-3-1 models the same leptonic bilinear contributes to the masses of both charged leptons and neutrinos, we develop an effective operator mechanism to generate mass for all leptons. The effective operators have dimension five for the case of charged leptons and dimension seven for neutrinos. By adding extra scalar multiplets and imposing the discrete symmetry Z(9)xZ(2) we are able to generate realistic textures for the leptonic mixing matrix. This mechanism requires new physics at the TeV scale.

Soft CP violation in K-meson systems

We consider a model with soft CP violation which accommodates the CP violation in the neutral kaons even if we assume that the Cabibbo-Kobayashi-Maskawa mixing matrix is real and the sources of CP violation are three complex vacuum expectation values and a trilinear coupling in the scalar potential. We show that for some reasonable values of the masses and other parameters the model allows us to explain all the observed CP violation processes in the K-0-(K) over bar (0) system.

Evidence for production of single top quarks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Determination of the Width of the Top Quark

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Neutrino masses and the scalar sector of a B-L extension of the standard model

We consider an electroweak model based on the gauge symmetry SU(2)(L) circle times U(1)(Y') circle times U(1)(B-L) which has right-handed neutrinos with different exotic B - L quantum numbers. Because of this particular feature we are able to write Yukawa terms, and right-handed neutrino mass terms, with scalar fields that can develop vacuum expectation values belonging to different energy scales. We make a detailed study of the scalar and the Yukawa neutrino sectors to show that this model is compatible with the observed solar and atmospheric neutrino mass scales and the tribimaximal mixing matrix. We also show that there are dark matter candidates if a Z(2) symmetry is included.

Improved determination of the width of the top quark

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)