Modified Chaplygin Gas and Solvable F-essence Cosmologies

The Modified Chaplygin Gas (MCG) model belongs to the class of a unified models of dark energy and dark matter. In this paper, we have modeled MCG in the framework of f-essence cosmology. By constructing an equation connecting the MCG and the f-essence, we solve it to obtain explicitly the pressure and energy density of MCG. As special cases, we obtain both positive and negative pressure solutions for suitable choices of free parameters. We also calculate the state parameter which describes the phantom crossing.Comment: 12 pages, (Invited Review), accepted for publication in "Astrophysics and Space Science" DOI: 10.1007/s10509-011-0870-

Space-time evolution induced by spinor fields with canonical and non-canonical kinetic terms

We study spinor field theories as an origin to induce space-time evolution. Self-interacting spinor fields with canonical and non-canonical kinetic terms are considered in a Friedman-Robertson-Walker universe. The deceleration parameter is calculated by solving the equation of motion and the Friedman equation, simultaneously. It is shown that the spinor fields can accelerate and decelerate the universe expansion. To construct realistic models we discuss the contributions from the dynamical symmetry breaking.Comment: 16 pages, 19 figure

Processos condicionantes de alterações em variáveis limnológicas: uma abordagem estatística na Represa de São Pedro, Juiz de Fora (MG)

RESUMO Os mananciais de abastecimento de água são ativos ambientais que precisam da atenção de toda a sociedade. O monitoramento de variáveis limnológicas possibilita inferir sobre as condições do recurso hídrico, além de oferecer indicativos de toda a dinâmica natural ou antrópica compreendida na bacia hidrográfica. A precipitação é um dos principais mecanismos atuantes nos parâmetros de qualidade de água, o que justifica sua relevância nesse tipo de análise. O teste t de Student e a análise fatorial/análise de componentes principais constituíram importantes ferramentas na interpretação dos dados limnológicos da captação da Represa de São Pedro, Juiz de Fora, Minas Gerais. O teste t de Student possibilitou verificar quais parâmetros apresentaram variação sazonal estatisticamente significativa. Já os resultados da análise fatorial/análise de componentes principais apontaram as variáveis mais relevantes na qualidade da água do manancial. A análise conjunta dos resultados estatísticos definiu os processos condicionantes das alterações nas variáveis estudadas, indicando o escoamento superficial como principal determinante das variáveis que compõem as componentes após rotação da matriz de componentes principais, Fator Varimax FV1 e FV4, e a contribuição orgânica, não associada à precipitação, como reflexo das variáveis da FV2 e FV3

Identification and reconstruction of low-energy electrons in the ProtoDUNE-SP detector

International audienceMeasurements of electrons from νe interactions are crucial for the Deep Underground Neutrino Experiment (DUNE) neutrino oscillation program, as well as searches for physics beyond the standard model, supernova neutrino detection, and solar neutrino measurements. This article describes the selection and reconstruction of low-energy (Michel) electrons in the ProtoDUNE-SP detector. ProtoDUNE-SP is one of the prototypes for the DUNE far detector, built and operated at CERN as a charged particle test beam experiment. A sample of low-energy electrons produced by the decay of cosmic muons is selected with a purity of 95%. This sample is used to calibrate the low-energy electron energy scale with two techniques. An electron energy calibration based on a cosmic ray muon sample uses calibration constants derived from measured and simulated cosmic ray muon events. Another calibration technique makes use of the theoretically well-understood Michel electron energy spectrum to convert reconstructed charge to electron energy. In addition, the effects of detector response to low-energy electron energy scale and its resolution including readout electronics threshold effects are quantified. Finally, the relation between the theoretical and reconstructed low-energy electron energy spectrum is derived and the energy resolution is characterized. The low-energy electron selection presented here accounts for about 75% of the total electron deposited energy. After the addition of missing energy using a Monte Carlo simulation, the energy resolution improves from about 40% to 25% at 50 MeV. These results are used to validate the expected capabilities of the DUNE far detector to reconstruct low-energy electrons

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours