Search for charged Higgs bosons through the violation of lepton universality in t art events using collision data at sqrts=7 TeV with the AT experiment

In several extensions of the Standard Model, the top quark can decay into a bottom quark and a light charged Higgs boson H+, t -> bH(+), in addition to the Standard Model decay t -> bW. Since W bosons decay to the three lepton generations equally, while H+ may predominantly decay into tau nu, charged Higgs bosons can be searched for using the violation of lepton universality in top quark decays. The analysis in this paper is based on 4.6 fb(-1) of proton-proton collision data at root s = 7 TeV collected by the ATLAS experiment at the Large Hadron Collider. Signatures containing leptons (e or mu) and/or a hadronically decaying tau (tau(had)) are used. Event yield ratios between e+ tau(had) and e + mu, as well as between mu + tau(had) and mu + e, final states are measured in the data and compared to predictions from simulations. This ratio-based method reduces the impact of systematic uncertainties in the analysis. No significant deviation from the Standard Model predictions is observed. With the assumption that the branching fraction B(H+ -> tau nu) is 100%, upper limits in the range 3.2%-4.4% can be placed on the branching fraction B(t -> bH(+)) for charged Higgs boson masses m(H+) in the range 90-140GeV. After combination with results from a search for charged Higgs bosons in t (t) over bar decays using the tau(had) + jets final state, upper limits on B(t -> bH(+)) can be set in the range 0.8%-3.4%, for m(H+) in the range 90-160GeV.

Search for a light charged Higgs boson in the decay channel H⁺ o c ars in t art events using collisions at sqrts = 7 TeV with the ATLAS detector

A search for a charged Higgs boson (H+) in t (t) over bar decays is presented, where one of the top quarks decays via t -> H(+)b, followed by H+ -> two jets (c (s) over bar). The other top quark decays to Wb, where the W boson then decays into a lepton (e/mu) and a neutrino. The data were recorded in pp collisions at root s = 7 TeV by the ATLAS detector at the LHC in 2011, and correspond to an integrated luminosity of 4.7 fb(-1). With no observation of a signal, 95 % confidence level (CL) upper limits are set on the decay branching ratio of top quarks to charged Higgs bosons varying between 5 % and 1 % for H+ masses between 90 GeV and 150 GeV, assuming B(H+ -> c (s) over bar) = 100 %.

Measurement of the TeV atmospheric muon charge ratio with the complete OPERA data set

The OPERA detector, designed to search for νμ → ντ oscillations in the CNGS beam, is located in the underground Gran Sasso laboratory, a privileged location to study TeV-scale cosmic rays. For the analysis here presented, the detector was used to measure the atmospheric muon charge ratio in the TeV region. OPERA collected chargeseparated cosmic ray data between 2008 and 2012. More than 3 million atmospheric muon events were detected and reconstructed, among which about 110000 multiple muon bundles. The charge ratio Rμ ≡ Nμ+/Nμ− was measured separately for single and for multiple muon events. The analysis exploited the inversion of the magnet polarity which was performed on purpose during the 2012 Run. The combination of the two data sets with opposite magnet polarities allowedminimizing systematic uncertainties and reaching an accurate determination of the muon charge ratio. Data were fitted to obtain relevant parameters on the composition of primary cosmic rays and the associated kaon production in the forward fragmentation region. In the surface energy range 1–20 TeV investigated by OPERA, Rμ is well described by a parametric model including only pion and kaon contributions to themuon flux, showing no significant contribution of the prompt component. The energy independence supports the validity of Feynman scaling in the fragmentation region up to 200 TeV/nucleon primary energy.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.