Searching for doubly-charged vector bileptons in the Golden Channel at the LHC

In this paper we investigate the LHC potential for discovering doubly-charged vector bileptons considering the measurable process $p,p$ $\rightarrow$ $e^{\mp}e^{\mp}\mu^{\pm}\mu^{\pm} X$. We perform the study using four different bilepton masses and three different exotics quark masses. Minimal LHC integrated luminosities needed for discovering and for setting limits on bilepton masses are obtained for both 7 TeV and 14 TeV center-of-mass energies. We find that these spectacular signatures can be observed at the LHC in the next years up to a bilepton mass of order of 1 TeV.Comment: 8 pages, 10 figure

Bounds on Z′Z^\prime from 3-3-1 model at the LHC energies

The Large Hadron Collider will restart with higher energy and luminosity in 2015. This achievement opens the possibility of discovering new phenomena hardly described by the Standard Model, that is based on two neutral gauge bosons: the photon and the $Z$. This perspective imposes a deep and systematic study of models that predicts the existence of new neutral gauge bosons. One of such models is based on the gauge group $SU(3)_C \times SU(3)_L \times U(1)_N$ called 3-3-1 model for short. In this paper we perform a study with $Z^\prime$ predicted in two versions of the 3-3-1 model and compare the signature of this resonance in each model version. By considering the present and future LHC energy regimes, we obtain some distributions and the total cross section for the process $p + p \longrightarrow \ell^{+} + \ell^{-} + X$. Additionally, we derive lower bounds on $Z^\prime$ mass from the latest LHC results. Finally we analyze the LHC potential for discovering this neutral gauge boson at 14 TeV center-of-mass energy.Comment: 6 pages, 9 figures, 2 table

Note on improvement precision of recursive function simulation in floating point standard

An improvement on precision of recursive function simulation in IEEE floating point standard is presented. It is shown that the average of rounding towards negative infinite and rounding towards positive infinite yields a better result than the usual standard rounding to the nearest in the simulation of recursive functions. In general, the method improves one digit of precision and it has also been useful to avoid divergence from a correct stationary regime in the logistic map. Numerical studies are presented to illustrate the method.Comment: DINCON 2017 - Conferencia Brasileira de Dinamica, Controle e Aplicacoes - Sao Jose do Rio Preto - Brazil. 8 page

A Data-Driven Machine Learning Approach for Electron-Molecule Ionization Cross Sections

Despite their importance in a wide variety of applications, the estimation of ionization cross sections for large molecules continues to present challenges for both experiment and theory. Machine learning algorithms have been shown to be an effective mechanism for estimating cross section data for atomic targets and a select number of molecular targets. We present an efficient machine learning model for predicting ionization cross sections for a broad array of molecular targets. Our model is a 3-layer neural network that is trained using published experimental datasets. There is minimal input to the network, making it widely applicable. We show that with training on as few as 10 molecular datasets, the network is able to predict the experimental cross sections of additional molecules with an accuracy similar to experimental uncertainties in existing data. As the number of training molecular datasets increased, the network's predictions became more accurate and, in the worst case, were within 30% of accepted experimental values. In many cases, predictions were within 10% of accepted values. Using a network trained on datasets for 25 different molecules, we present predictions for an additional 27 molecules, including alkanes, alkenes, molecules with ring structures, and DNA nucleotide bases

Subtracting and Fitting Histograms using Profile Likelihood

It is known that many interesting signals expected at LHC are of unknown shape and strongly contaminated by background events. These signals will be dif cult to detect during the rst years of LHC operation due to the initial low luminosity. In this work, one presents a method of subtracting histograms based on the pro le likelihood function when the background is previously estimated by Monte Carlo events and one has low statistics. Estimators for the signal in each bin of the histogram difference are calculated so as limits for the signals with 68.3% of Con dence Level in a low statistics case when one has a exponential background and a Gaussian signal. The method can also be used to t histograms when the signal shape is known. Our results show a good performance and avoid the problem of negative values when subtracting histograms