Measurement of identified charged hadron spectra with the ALICE experiment at the LHC

The ALICE experiment features multiple particle identification systems. The measurement of the identified charged hadron $p_{t}$ spectra in proton-proton collisions at $\sqrt{s}=900$ GeV will be discussed. In the central rapidity region ($|\eta|<0.9$) particle identification and tracking are performed using the Inner Tracking System (ITS), which is the closest detector to the beam axis, the Time Projection Chamber (TPC) and a dedicated time-of-flight system (TOF). Particles are mainly identified using the energy loss signal in the ITS and TPC. In addition, the information from TOF is used to identify hadrons at higher momenta. Finally, the kink topology of the weak decay of charged kaons provides an alternative method to extract the transverse momentum spectra of charged kaons. This combination allows to track and identify charged hadrons in the transverse momentum ($p_{t}$) range from 100 MeV/c up to 2.5 GeV/$c$. Mesons containing strange quarks (\kos, $\phi$) and both singly and doubly strange baryons (\lam, \lambar, and \xip + \xim) are identified by their decay topology inside the TPC detector. Results obtained with the various identification tools above described and a comparison with theoretical models and previously published data will be presented.Comment: 11 pages, 14 figures, contribution to conference proceedings of the 27th Winter Workshop on Nuclear Dynamic

Transverse momentum spectra of hadrons identified with the ALICE Inner Tracking System

The Inner Tracking System is the ALICE detector closest to the beam axis. It is composed of six layers of silicon detectors: two innermost layers of Silicon Pixel Detectors (SPD), two intermediate layers of Silicon Drift Detectors (SDD) and two outermost layers of Silicon Strip Detectors (SSD). The ITS can be used as a standalone tracker in order to recover tracks that are not reconstructed by the Time Projection Chamber (TPC) and to reconstruct low momentum particles with $p_{t}$ down to 100 MeV/c. Particle identification in the ITS is performed by measuring the energy loss signal in the SDD and SSD layers. The ITS allows to extend the charged particle identification capability in the ALICE central rapidity region at low $p_{t}$: it is possible to separate $\pi/K$ in the range 100 MeV/c $< p_{t} <$ 500 MeV/c and $K/p$ in the range 200 MeV/c $< p_{t} <$ 800 MeV/c. The identification of hadron in the ITS will be discussed in detail, different methods used to extract the $p_{t}$ spectra of $\pi, K$ and $p$ will also be described.Comment: 2 pages, 2 figures, submitted as contribution to PLHC2011 conference proceeding

Two-particle correlations in p-Pb collisions at the LHC with ALICE

The double ridge structure previously observed in Pb-Pb collisions has also been recently observed in high-multiplicity p-Pb collisions at sqrt(s_NN) = 5.02 TeV. These systems show a long-range structure (large separation in Delta_eta) at the near- (Delta_phi ~ 0) and away-side (Delta_phi ~ pi) of the trigger particle. In order to understand the nature of this effect the two-particle correlation analysis has been extended to identified particles. Particles are identified up to transverse momentum pT values of 4 GeV/c using the energy loss signal in the Time Projection Chamber detector, complemented with the information from the Time of Flight detector. This measurement casts a new light on the potential collective (i.e. hydrodynamic) behaviour of particle production in p-Pb collisions.Comment: 4 pages, 3 figures, Proceedings of Strangeness in Quark Matter 2013 conference, 21-27 July 201

Hybrid Neural Networks for Frequency Estimation of Unevenly Sampled Data

In this paper we present a hybrid system composed by a neural network based estimator system and genetic algorithms. It uses an unsupervised Hebbian nonlinear neural algorithm to extract the principal components which, in turn, are used by the MUSIC frequency estimator algorithm to extract the frequencies. We generalize this method to avoid an interpolation preprocessing step and to improve the performance by using a new stop criterion to avoid overfitting. Furthermore, genetic algorithms are used to optimize the neural net weight initialization. The experimental results are obtained comparing our methodology with the others known in literature on a Cepheid star light curve.Comment: 5 pages, to appear in the proceedings of IJCNN 99, IEEE Press, 199

Coronal MHD transport theory and phenomenology

In the presence of a weakly inhomogeneous background, magnetohydrodynamic fluctuations are transported, reflected and at small scales, dissipated. In contrast to orderings appropriate to outer solar wind conditions, here we explore transport in a regime relevant for solar coronal heating and solar wind acceleration, in which effects of the order of the Alfvén speed are retained while disregarding the solar wind velocity. We consider the general properties of the transport equations as well as some solutions of interest

Conditions for sustainment of magnetohydrodynamic turbulence driven by Alfvén waves

In a number of space and astrophysical plasmas,turbulence is driven by the supply of wave energy. In the context of incompressible magnetohydrodynamics (MHD) there are basic physical reasons, associated with conservation of cross helicity, why this kind of driving may be ineffective in sustaining turbulence. Here an investigation is made into some basic requirements for sustaining steady turbulence and dissipation in the context of incompressible MHD in a weakly inhomogeneous open field line region, driven by the supply of unidirectionally propagating waves at a boundary. While such wave driving cannot alone sustain turbulence, the addition of reflection permits sustainment. Another sustainment issue is the action of the nonpropagating or quasi-two dimensional part of the spectrum; this is particularly important in setting up a steady cascade. Thus, details of the waveboundary conditions also affect the ease of sustaining a cascade. Supply of a broadband spectrum of waves can overcome the latter difficulty but not the former, that is, the need for reflections. Implications for coronal heating and other astrophysical applications, as well as simulations, are suggested

IIR Adaptive Filters for Detection of Gravitational Waves from Coalescing Binaries

In this paper we propose a new strategy for gravitational waves detection from coalescing binaries, using IIR Adaptive Line Enhancer (ALE) filters. This strategy is a classical hierarchical strategy in which the ALE filters have the role of triggers, used to select data chunks which may contain gravitational events, to be further analyzed with more refined optimal techniques, like the the classical Matched Filter Technique. After a direct comparison of the performances of ALE filters with the Wiener-Komolgoroff optimum filters (matched filters), necessary to discuss their performance and to evaluate the statistical limitation in their use as triggers, we performed a series of tests, demonstrating that these filters are quite promising both for the relatively small computational power needed and for the robustness of the algorithms used. The performed tests have shown a weak point of ALE filters, that we fixed by introducing a further strategy, based on a dynamic bank of ALE filters, running simultaneously, but started after fixed delay times. The results of this global trigger strategy seems to be very promising, and can be already used in the present interferometers, since it has the great advantage of requiring a quite small computational power and can easily run in real-time, in parallel with other data analysis algorithms.Comment: Accepted at SPIE: "Astronomical Telescopes and Instrumentation". 9 pages, 3 figure

A cellular automaton for the factor of safety field in landslides modeling

Landslide inventories show that the statistical distribution of the area of recorded events is well described by a power law over a range of decades. To understand these distributions, we consider a cellular automaton to model a time and position dependent factor of safety. The model is able to reproduce the complex structure of landslide distribution, as experimentally reported. In particular, we investigate the role of the rate of change of the system dynamical variables, induced by an external drive, on landslide modeling and its implications on hazard assessment. As the rate is increased, the model has a crossover from a critical regime with power-laws to non power-law behaviors. We suggest that the detection of patterns of correlated domains in monitored regions can be crucial to identify the response of the system to perturbations, i.e., for hazard assessment.Comment: 4 pages, 3 figure

A Case Study of the Ethanol CleanCook Stove Intervention and Potential Scale-Up in Ethiopia

Background Approximately 80% of Ethiopia\u27s energy consumption is dominated by woody biomass fuel use, resulting in 91.2 million tons of firewood and 4.2 million tons of charcoal consumed annually. Ethiopia\u27s dependency on non-sustainable energy, especially for cooking, has been a major concern for the nation for the past 30 years, contributing to deforestation, climate change, and adverse human health impacts. Objectives Our objective was to document the work of Gaia Association and the implementation of the ethanol CleanCook stove in the refugee camp and urban settings of Ethiopia. We then assessed the potential for the scale-up of ethanol as a household fuel. Methods We utilized the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework to evaluate the effectiveness and sustainability of the ethanol cookstove intervention. We obtained secondary data from a variety of sources to evaluate a.) The performance of the CleanCook ethanol stove; b.) Effectiveness of the ethanol cookstove implementation; and, c.) Barriers to scale-up and commercialization of ethanol use as a household fuel. In addition, we conducted primary analysis of qualitative surveys to evaluate the perceptions of the ethanol and adoption of the CleanCook stove. Results Our case study results provide critical insight into the 13-year implementation of the CleanCook ethanol stove in Ethiopia. Laboratory tests demonstrate that the CleanCook stove reduces harmful emissions compared to biomass stoves, and preliminary field tests show 24-hour average PM2.5 levels of 200 μg/m3. To-date 8731 CleanCook stoves were distributed to refugee households, while an additional 500 were sold at a subsided price to low-income urban households. CleanCook stove users report the continued use of multiple stoves. Conclusions The CleanCook ethanol stove has been implemented as an energy intervention for the vulnerable refugee population in Ethiopia for over 13 years. There has been limited success of a subsidized CleanCook stove among low-income households in Addis Ababa. This case study demonstrates the complexities of promoting a new fuel for household cooking, and the numerous obstacles and stagnations in implementation. Ethanol demonstrates some potential for scale-up and commercialization as a household fuel in Addis Ababa, but it may require simultaneous stabilization of ethanol supply, growth of a city-wide distribution infrastructure, and an affordably priced stove and fuel