6,788 research outputs found
Simulation of UHE muons propagation for GEANT3
A simulation package for the transport of high energy muons has been
developed. It has been conceived to replace the muon propagation software
modules implemented in the detector simulation program GEANT3. Here we discuss
the results achieved with our package and we check the agreement with numerical
calculations up to 10**8 GeV.Comment: 21 pages, 6 figures, 1 Table. AMSTeX document, acknowledgments adde
Linear Theory of Electron-Plasma Waves at Arbitrary Collisionality
The dynamics of electron-plasma waves are described at arbitrary
collisionality by considering the full Coulomb collision operator. The
description is based on a Hermite-Laguerre decomposition of the velocity
dependence of the electron distribution function. The damping rate, frequency,
and eigenmode spectrum of electron-plasma waves are found as functions of the
collision frequency and wavelength. A comparison is made between the
collisionless Landau damping limit, the Lenard-Bernstein and Dougherty
collision operators, and the electron-ion collision operator, finding large
deviations in the damping rates and eigenmode spectra. A purely damped entropy
mode, characteristic of a plasma where pitch-angle scattering effects are
dominant with respect to collisionless effects, is shown to emerge numerically,
and its dispersion relation is analytically derived. It is shown that such a
mode is absent when simplified collision operators are used, and that
like-particle collisions strongly influence the damping rate of the entropy
mode.Comment: 23 pages, 10 figures, accepted for publication on Journal of Plasma
Physic
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
An explosion of high-throughput DNA sequencing in the past decade has led to
a surge of interest in population-scale inference with whole-genome data.
Recent work in population genetics has centered on designing inference methods
for relatively simple model classes, and few scalable general-purpose inference
techniques exist for more realistic, complex models. To achieve this, two
inferential challenges need to be addressed: (1) population data are
exchangeable, calling for methods that efficiently exploit the symmetries of
the data, and (2) computing likelihoods is intractable as it requires
integrating over a set of correlated, extremely high-dimensional latent
variables. These challenges are traditionally tackled by likelihood-free
methods that use scientific simulators to generate datasets and reduce them to
hand-designed, permutation-invariant summary statistics, often leading to
inaccurate inference. In this work, we develop an exchangeable neural network
that performs summary statistic-free, likelihood-free inference. Our framework
can be applied in a black-box fashion across a variety of simulation-based
tasks, both within and outside biology. We demonstrate the power of our
approach on the recombination hotspot testing problem, outperforming the
state-of-the-art.Comment: 9 pages, 8 figure
Visibility analysis of boundary layer transition
We study the transition to turbulence in a flat plate boundary layer by means of visibility analysis of velocity time-series extracted across the flow domain. By taking into account the mutual visibility of sampled values, visibility graphs are constructed from each time series. The latter are, thus, transformed into a geometrical object, whose main features can be explored using measures typical of network science that provide a reduced order representation of the underlying flow properties. Using these metrics, we observe the evolution of the flow from laminarity to turbulence and the effects exerted by the free-stream turbulence. Different from other methods requiring an extensive amount of spatiotemporal data (e.g., full velocity field) or a set of parameters and thresholds arbitrarily chosen by the user, the present network-based approach is able to identify the onset markers for transition by means of the streamwise velocity time-series alone. Published under an exclusive license by AIP Publishing
Update on viral infections involving the central nervous system in pediatric patients
Infections of the central nervous system (CNS) are mainly caused by viruses, and these infections can be life-threatening in pediatric patients. Although the prognosis of CNS infections is often favorable, mortality and long-term sequelae can occur. The aims of this narrative review were to describe the specific microbiological and clinical features of the most frequent pathogens and to provide an update on the diagnostic approaches and treatment strategies for viral CNS infections in children. A literature analysis showed that the most common pathogens worldwide are enteroviruses, arboviruses, parechoviruses, and herpesviruses, with variable prevalence rates in different countries. Lumbar puncture (LP) should be performed as soon as possible when CNS infection is suspected, and cerebrospinal fluid (CSF) samples should always be sent for polymerase chain reaction (PCR) analysis. Due to the lack of specific therapies, the management of viral CNS infections is mainly based on supportive care, and empiric treatment against herpes simplex virus (HSV) infection should be started as soon as possible. Some researchers have questioned the role of acyclovir as an empiric antiviral in older children due to the low incidence of HSV infection in this population and observed that HSV encephalitis may be clinically recognizable beyond neonatal age. However, the real benefit-risk ratio of selective approaches is unclear, and further studies are needed to define appropriate indications for empiric acyclovir. Research is needed to find specific therapies for emerging pathogens. Moreover, the appropriate timing of monitoring neurological development, performing neuroimaging evaluations and investigating the effectiveness of rehabilitation during follow-up should be evaluated with long-term studies
Fluorescence and Hybrid Detection Aperture of the Pierre Auger Observatory
The aperture of the Fluorescence Detector (FD) of the Pierre Auger
Observatory is evaluated from simulated events using different detector
configurations: mono, stereo, 3-FD and 4-FD. The trigger efficiency has been
modeled using shower profiles with ground impacts in the field of view of a
single telescope and studying the trigger response (at the different levels) by
that telescope and by its neighbours. In addition, analysis cuts imposed by
event reconstruction have been applied. The hybrid aperture is then derived for
the Auger final extension. Taking into account the actual Surface Detector (SD)
array configuration and its trigger response, the aperture is also calculated
for a typical configuration of the present phase.Comment: contribution to the 29th International Cosmic Ray Conference, Pune,
India, 3-10 August 200
All optical implementation of a stochastic logic gate using a VCSEL with external optical injection
We perform numerical simulations of the dynamics of two VCSELs optically coupled in a master-slave configuration. We show that the interplay of nonlinearity and spontaneous emission noise can yield logic behavior, and the emergent outcome of such system is a reliable logic gate. Specifically in our case represents an all-optical logic gate, with the logic input encoded in the strength of the light injected in the slave laser by the master laser, and the fast and robust response decoded from the polarization state of the light emitted by the slave laser.Peer ReviewedPostprint (author’s final draft
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