5,102 research outputs found
Finite-size scaling and deconfinement transition: the case of 4D SU(2) pure gauge theory
A recently introduced method for determining the critical indices of the
deconfinement transition in gauge theories, already tested for the case of 3D
SU(3) pure gauge theory, is applied here to 4D SU(2) pure gauge theory. The
method is inspired by universality and based on the finite size scaling
behavior of the expectation value of simple lattice operators, such as the
plaquette. We obtain an accurate determination of the critical index , in
agreement with the prediction of the Svetitsky-Yaffe conjecture.Comment: 11 pages, 3 eps figure
A new veto for continuous gravitational wave searches
We present a new veto procedure to distinguish between continuous
gravitational wave (CW) signals and the detector artifacts that can mimic their
behavior. The veto procedure exploits the fact that a long-lasting coherent
disturbance is less likely than a real signal to exhibit a Doppler modulation
of astrophysical origin. Therefore, in the presence of an outlier from a
search, we perform a multi-step search around the frequency of the outlier with
the Doppler modulation turned off (DM-off), and compare these results with the
results from the original (DM-on) search. If the results from the DM-off search
are more significant than those from the DM-on search, the outlier is most
likely due to an artifact rather than a signal. We tune the veto procedure so
that it has a very low false dismissal rate. With this veto, we are able to
identify as coherent disturbances >99.9% of the 6349 candidates from the recent
all-sky low-frequency Einstein@Home search on the data from the Advanced LIGO
O1 observing run [1]. We present the details of each identified disturbance in
the Appendix.Comment: 10 pages, 6 figures, 2 table
Dynamic versus Static Structure Functions and Novel Diffractive Effects in QCD
Initial- and final-state rescattering, neglected in the parton model, have a
profound effect in QCD hard-scattering reactions, predicting single-spin
asymmetries, diffractive deep inelastic scattering, diffractive hard hadronic
reactions, the breakdown of the Lam Tung relation in Drell-Yan reactions, and
nuclear shadowing and non-universal antishadowing--leading-twist physics not
incorporated in the light-front wavefunctions of the target computed in
isolation. I also discuss the use of diffraction to materialize the Fock states
of a hadronic projectile and test QCD color transparency, and anomalous heavy
quark effects. The presence of direct higher-twist processes where a proton is
produced in the hard subprocess can explain the large proton-to-pion ratio seen
in high centrality heavy ion collisions. I emphasize the importance of
distinguishing between static observables such as the probability distributions
computed from the square of the light-front wavefunctions versus dynamical
observables which include the effects of rescattering.Comment: 8 pages, 1 figure. Presented at Diffraction 2008: International
Workshop On Diffraction In High Energy Physics 9-14 Sep 2008, La
Londe-les-Maures, Franc
Random template banks and relaxed lattice coverings
Template-based searches for gravitational waves are often limited by the
computational cost associated with searching large parameter spaces. The study
of efficient template banks, in the sense of using the smallest number of
templates, is therefore of great practical interest. The "traditional" approach
to template-bank construction requires every point in parameter space to be
covered by at least one template, which rapidly becomes inefficient at higher
dimensions. Here we study an alternative approach, where any point in parameter
space is covered only with a given probability < 1. We find that by giving up
complete coverage in this way, large reductions in the number of templates are
possible, especially at higher dimensions. The prime examples studied here are
"random template banks", in which templates are placed randomly with uniform
probability over the parameter space. In addition to its obvious simplicity,
this method turns out to be surprisingly efficient. We analyze the statistical
properties of such random template banks, and compare their efficiency to
traditional lattice coverings. We further study "relaxed" lattice coverings
(using Zn and An* lattices), which similarly cover any signal location only
with probability < 1. The relaxed An* lattice is found to yield the most
efficient template banks at low dimensions (n < 10), while random template
banks increasingly outperform any other method at higher dimensions.Comment: 13 pages, 10 figures, submitted to PR
Features of elastic scattering at small t at the LHC
The problems linked with the extraction of the basic parameters of the hadron
elastic scattering amplitude at the LHC are explored. It is shown that one
should take into account the saturation regime which will lead to new effects
at the LHC.Comment: 3. pages, 6 figures, talk on the International workshop on
"Diffraction in High Energy Physics", La Londe-les-Maures, France (2008
A nature-inspired feature selection approach based on hypercomplex information
Feature selection for a given model can be transformed into an optimization task. The essential idea behind it is to find the most suitable subset of features according to some criterion. Nature-inspired optimization can mitigate this problem by producing compelling yet straightforward solutions when dealing with complicated fitness functions. Additionally, new mathematical representations, such as quaternions and octonions, are being used to handle higher-dimensional spaces. In this context, we are introducing a meta-heuristic optimization framework in a hypercomplex-based feature selection, where hypercomplex numbers are mapped to real-valued solutions and then transferred onto a boolean hypercube by a sigmoid function. The intended hypercomplex feature selection is tested for several meta-heuristic algorithms and hypercomplex representations, achieving results comparable to some state-of-the-art approaches. The good results achieved by the proposed approach make it a promising tool amongst feature selection research
Data analysis of gravitational-wave signals from spinning neutron stars. V. A narrow-band all-sky search
We present theory and algorithms to perform an all-sky coherent search for
periodic signals of gravitational waves in narrow-band data of a detector. Our
search is based on a statistic, commonly called the -statistic,
derived from the maximum-likelihood principle in Paper I of this series. We
briefly review the response of a ground-based detector to the
gravitational-wave signal from a rotating neuron star and the derivation of the
-statistic. We present several algorithms to calculate efficiently
this statistic. In particular our algorithms are such that one can take
advantage of the speed of fast Fourier transform (FFT) in calculation of the
-statistic. We construct a grid in the parameter space such that
the nodes of the grid coincide with the Fourier frequencies. We present
interpolation methods that approximately convert the two integrals in the
-statistic into Fourier transforms so that the FFT algorithm can
be applied in their evaluation. We have implemented our methods and algorithms
into computer codes and we present results of the Monte Carlo simulations
performed to test these codes.Comment: REVTeX, 20 pages, 8 figure
Handling dropout probability estimation in convolution neural networks using meta-heuristics
Deep learning-based approaches have been paramount in recent years, mainly due to their outstanding results in several application domains, ranging from face and object recognition to handwritten digit identification. Convolutional Neural Networks (CNN) have attracted a considerable attention since they model the intrinsic and complex brain working mechanisms. However, one main shortcoming of such models concerns their overfitting problem, which prevents the network from predicting unseen data effectively. In this paper, we address this problem by means of properly selecting a regularization parameter known as Dropout in the context of CNNs using meta-heuristic-driven techniques. As far as we know, this is the first attempt to tackle this issue using this methodology. Additionally, we also take into account a default dropout parameter and a dropout-less CNN for comparison purposes. The results revealed that optimizing Dropout-based CNNs is worthwhile, mainly due to the easiness in finding suitable dropout probability values, without needing to set new parameters empirically
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