Combined search for the Higgs boson with the D0 experiment

We perform a combination of searches for standard model Higgs boson production in $p\bar{p}$ collisions recorded by the D0 detector at the Fermilab Tevatron Collider at a center of mass energy of $\sqrt{s}=1.96$ TeV. The different production and decay channels have been analyzed separately, with integrated luminosities of up to 9.7 fb$^{-1}$ and for Higgs boson masses $90\leq M_H \leq 200$ GeV. We combine these final states to achieve optimal sensitivity to the production of the Higgs boson. We also interpret the combination in terms of models with a fourth generation of fermions, and models with suppressed Higgs boson couplings to fermions. The result excludes a standard model Higgs boson at 95% C.L. in the ranges $90 M_HM_

On the energy and baseline optimization to study effects related to the δ\delta-phase (CP-/T-violation) in neutrino oscillations at a Neutrino Factory

In this paper we discuss the detection of CP and T-violation effects in the framework of a neutrino factory. We introduce three quantities, which are good discriminants for a non vanishing complex phase ($\delta$) in the $3\times 3$ neutrino mixing matrix. We find that these three discriminants (in vacuum) all scale with $L/E_{\nu}$. Matter effects modify the scaling, but these effects are large enough to spoil the sensitivity only for baselines larger than 5000 km. So, in the hypothesis of constant neutrino factory power, the sensitivity on the $\delta$-phase is independent of the baseline chosen. Specially interesting is the direct measurement of T-violation from the ``wrong-sign'' electron channel, which involves a comparison of the \nue\ra\numu and \numu\ra\nue oscillation rates. However, the \numu\ra\nue measurement requires magnetic discrimination of the electron charge, experimentally very challenging in a neutrino detector: low-energy neutrino beams and hence short baselines, are preferred. In this paper we show the exclusion regions in the $\Delta m^2_{12} - \delta$ plane for two concrete cases. We obtain a similar excluded region provided that the electron detection efficiency is $\sim$20% and the charge confusion 0.1%. The $\Delta m^2_{12}$ compatible with the LMA solar data can be tested with a flux of 5$\times 10^{21}$ muons. We compare these results with the fit of the visible energy distributions.Comment: 58 pages, 24 figure

A CASE STUDY ON SUPPORT VECTOR MACHINES VERSUS ARTIFICIAL NEURAL NETWORKS

The capability of artificial neural networks for pattern recognition of real world problems is well known. In recent years, the support vector machine has been advocated for its structure risk minimization leading to tolerance margins of decision boundaries. Structures and performances of these pattern classifiers depend on the feature dimension and training data size. The objective of this research is to compare these pattern recognition systems based on a case study. The particular case considered is on classification of hypertensive and normotensive right ventricle (RV) shapes obtained from Magnetic Resonance Image (MRI) sequences. In this case, the feature dimension is reasonable, but the available training data set is small, however, the decision surface is highly nonlinear.For diagnosis of congenital heart defects, especially those associated with pressure and volume overload problems, a reliable pattern classifier for determining right ventricle function is needed. RV¡¦s global and regional surface to volume ratios are assessed from an individual¡¦s MRI heart images. These are used as features for pattern classifiers. We considered first two linear classification methods: the Fisher linear discriminant and the linear classifier trained by the Ho-Kayshap algorithm. When the data are not linearly separable, artificial neural networks with back-propagation training and radial basis function networks were then considered, providing nonlinear decision surfaces. Thirdly, a support vector machine was trained which gives tolerance margins on both sides of the decision surface. We have found in this case study that the back-propagation training of an artificial neural network depends heavily on the selection of initial weights, even though randomized. The support vector machine where radial basis function kernels are used is easily trained and provides decision tolerance margins, in spite of only small margins

Precision studies of the Higgs boson decay channel H -> ZZ -> 4l with MEKD

The importance of the H -> ZZ -> 4l "golden" channel was shown by its major role in the discovery, by the ATLAS and CMS collaborations, of a Higgs-like boson with mass near 125 GeV. We analyze the discrimination power of the matrix element method both for separating the signal from the irreducible ZZ background and for distinguishing various spin and parity hypotheses describing a signal in this channel. We show that the proper treatment of interference effects associated with permutations of identical leptons in the four electron and four muon final states plays an important role in achieving the best sensitivity in measuring the properties of the newly discovered boson. We provide a code, MEKD, that calculates kinematic discriminants based on the full leading order matrix elements and which will aid experimentalists and phenomenologists in their continuing studies of the H -> ZZ -> 4l channel.Comment: Major revision: added new sections discussing spin/ parity determination and the importance of using the full matrix element for the same flavor final state (involving both pairings of the leptons). Also added new functionality, including the most general couplings of a spin-0 or spin-2 boson to gluons and Zs, to the publicly-available code, MEKD, presented in this paper. 43 pages, 15 figure