Do Young Neutron Stars Which Show Themselves As AXPs, SGRs and Radio Pulsars Accrete?

We examined the fall-back disk models, and in general accretion, proposed to explain the properties of anomalous X-ray pulsars (AXPs), soft gamma repeaters (SGRs), and radio pulsars (PSRs). We checked the possibility of some gas remaining around the neutron star after the supernova explosion. We also compared AXPs and SGRs with the X-ray pulsars in X-ray binaries. We conclude the existing models of accretion from a fall-back disk are insufficient to explain the nature of AXPs/SGRs, particularly the SGR bursts. We also discussed the proposed model of combination of magnetic dipole radiation and propeller torques in order to explain the evolution of radio pulsars on the P-\.{P} diagram. The predictions of this model contradict the observational data.Comment: 16 Pages, 1 Figur

Emitter Location Finding using Particle Swarm Optimization

Using several spatially separated receivers, nowadays positioning techniques, which are implemented to determine the location of the transmitter, are often required for several important disciplines such as military, security, medical, and commercial applications. In this study, localization is carried out by particle swarm optimization using time difference of arrival. In order to increase the positioning accuracy, time difference of arrival averaging based two new methods are proposed. Results are compared with classical algorithms and Cramer-Rao lower bound which is the theoretical limit of the estimation error

A Linear Iterative Unfolding Method

A frequently faced task in experimental physics is to measure the probability distribution of some quantity. Often this quantity to be measured is smeared by a non-ideal detector response or by some physical process. The procedure of removing this smearing effect from the measured distribution is called unfolding, and is a delicate problem in signal processing, due to the well-known numerical ill behavior of this task. Various methods were invented which, given some assumptions on the initial probability distribution, try to regularize the unfolding problem. Most of these methods definitely introduce bias into the estimate of the initial probability distribution. We propose a linear iterative method, which has the advantage that no assumptions on the initial probability distribution is needed, and the only regularization parameter is the stopping order of the iteration, which can be used to choose the best compromise between the introduced bias and the propagated statistical and systematic errors. The method is consistent: "binwise" convergence to the initial probability distribution is proved in absence of measurement errors under a quite general condition on the response function. This condition holds for practical applications such as convolutions, calorimeter response functions, momentum reconstruction response functions based on tracking in magnetic field etc. In presence of measurement errors, explicit formulae for the propagation of the three important error terms is provided: bias error, statistical error, and systematic error. A trade-off between these three error terms can be used to define an optimal iteration stopping criterion, and the errors can be estimated there. We provide a numerical C library for the implementation of the method, which incorporates automatic statistical error propagation as well.Comment: Proceedings of ACAT-2011 conference (Uxbridge, United Kingdom), 9 pages, 5 figures, changes of corrigendum include

CMF-DFE Based Adaptive Blind Equalization Using Particle Swarm Optimization

The channel matched filter (CMF) is the optimum receiver providing the maximum signal to noise ratio (SNR) for the frequency selective channels. The output intersymbol interference (ISI) profile of the CMF convolved by the channel can be blindly obtained by using the autocorrelation of the received signal. Therefore, the inverse of the autocorrelation function can be used to equalize the channel passed through its own CMF. The only missing part to complete the proposed blind operation is the CMF coefficients. Therefore, in this work, the best training algorithm investigation is subjected for blind estimation of the CMF coefficients. The proposed method allows using more effective training algorithms for blind equalizations. However, the expected high performance training is obtained when the swarm intelligence is used. Unlike the stochastic gradient algorithms, the particle swarm optimization (PSO) is known to have fast convergence because its performance is independent of the characteristics of the systems used. The obtained mean square error (MSE) and bit error rate (BER) performances are promising for high performance real-time systems as an alternative to non-blind equalization techniques

Association among SNAP-25 gene DdeI and MnlI polymorphisms and hemodynamic changes during methylphenidate use: A functional near-infrared spectroscopy study

Objective: To investigate the interaction of treatment-related hemodynamic changes with genotype status for Synaptosomal associated protein 25 (SNAP-25) gene in participants with attention deficit hyperactivity disorder (ADHD) on and off single dose short-acting methylphenidate treatment with functional near-infrared spectroscopy (fNIRS). Method: A total of 15 right-handed adults and 16 right-handed children with DSM-IV diagnosis of ADHD were evaluated. Ten milligrams of short-acting methylphenidate was administered in a crossover design. Results: Participants with SNAP-25 DdeI T/T genotype had decreased right deoxyhemoglobin ([HHb]) with treatment. SNAP-25 MnlI genotype was also associated with right deoxyhemoglobin ([HbO2]) and [HHb] changes as well as left [HHb] change. When the combinations of these genotypes were taken into account, the participants with [DdeI C/C or T/C and MnlI G/G or T/G] genotype had increased right [HHb] with MPH use whereas the participants with [DdeI T/T and MnlI T/T] or [DdeI T/T and MnlI G/G or T/G] genotypes had decreased right prefrontal [HHb]. Conclusions: These results suggested that SNAP-25 polymorphism might be associated with methylphenidate induced brain hemodynamic changes in ADHD participants. © 2011 SAGE Publications

Simulation of the CMS Resistive Plate Chambers

The Resistive Plate Chamber (RPC) muon subsystem contributes significantly to the formation of the trigger decision and reconstruction of the muon trajectory parameters. Simulation of the RPC response is a crucial part of the entire CMS Monte Carlo software and directly influences the final physical results. An algorithm based on the parametrization of RPC efficiency, noise, cluster size and timing for every strip has been developed. Experimental data obtained from cosmic and proton-proton collisions at $\sqrt{s}=7$ TeV have been used for determination of the parameters. A dedicated validation procedure has been developed. A good agreement between the simulated and experimental data has been achieved.Comment: to be published in JINS

Measurement of masses in the [Formula: see text] system by kinematic endpoints in pp collisions at [Formula: see text].

A simultaneous measurement of the top-quark, W-boson, and neutrino masses is reported for [Formula: see text] events selected in the dilepton final state from a data sample corresponding to an integrated luminosity of 5.0 fb-1 collected by the CMS experiment in pp collisions at [Formula: see text]. The analysis is based on endpoint determinations in kinematic distributions. When the neutrino and W-boson masses are constrained to their world-average values, a top-quark mass value of [Formula: see text] is obtained. When such constraints are not used, the three particle masses are obtained in a simultaneous fit. In this unconstrained mode the study serves as a test of mass determination methods that may be used in beyond standard model physics scenarios where several masses in a decay chain may be unknown and undetected particles lead to underconstrained kinematics