39,521 research outputs found
CMB power spectrum contribution from cosmic strings using field-evolution simulations of the Abelian Higgs model
We present the first field-theoretic calculations of the contribution made by
cosmic strings to the temperature power spectrum of the cosmic microwave
background (CMB). Unlike previous work, in which strings were modeled as
idealized one-dimensional objects, we evolve the simplest example of an
underlying field theory containing local U(1) strings, the Abelian Higgs model.
Limitations imposed by finite computational volumes are overcome using the
scaling property of string networks and a further extrapolation related to the
lessening of the string width in comoving coordinates. The strings and their
decay products, which are automatically included in the field theory approach,
source metric perturbations via their energy-momentum tensor, the unequal-time
correlation functions of which are used as input into the CMB calculation
phase. These calculations involve the use of a modified version of CMBEASY,
with results provided over the full range of relevant scales. We find that the
string tension required to normalize to the WMAP 3-year data at multipole
is , where we have quoted statistical and systematic errors
separately, and is Newton's constant. This is a factor 2-3 higher than
values in current circulation.Comment: 23 pages, 14 figures; further optimized figures for 1Mb size limit,
appendix added before submission to journal, matches accepted versio
String Tension and Thermodynamics with Tree Level and Tadpole Improved Actions
We calculate the string tension, deconfinement transition temperature and
bulk thermodynamic quantities of the SU(3) gauge theory using tree level and
tadpole improved actions. Finite temperature calculations have been performed
on lattices with temporal extent N_tau = 3 and 4. Compared to calculations with
the standard Wilson action on this size lattices we observe a drastic reduction
of the cut-off dependence of bulk thermodynamic observables at high
temperatures. In order to test the influence of improvement on long-distance
observables at T_c we determine the ratio T_c/sqrt(sigma). For all actions,
including the standard Wilson action, we find results which differ only little
from each other. We do, however, observe an improved asymptotic scaling
behaviour for the tadpole improved action compared to the Wilson and tree level
improved actions.Comment: 20 pages, LaTeX2e File, 8 coloured Postscript figures, new analysis
added, recent Wilson action string tension results included, figures replace
Modeling and interpolation of the ambient magnetic field by Gaussian processes
Anomalies in the ambient magnetic field can be used as features in indoor
positioning and navigation. By using Maxwell's equations, we derive and present
a Bayesian non-parametric probabilistic modeling approach for interpolation and
extrapolation of the magnetic field. We model the magnetic field components
jointly by imposing a Gaussian process (GP) prior on the latent scalar
potential of the magnetic field. By rewriting the GP model in terms of a
Hilbert space representation, we circumvent the computational pitfalls
associated with GP modeling and provide a computationally efficient and
physically justified modeling tool for the ambient magnetic field. The model
allows for sequential updating of the estimate and time-dependent changes in
the magnetic field. The model is shown to work well in practice in different
applications: we demonstrate mapping of the magnetic field both with an
inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.Comment: 17 pages, 12 figures, to appear in IEEE Transactions on Robotic
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
This paper presents a novel generative model to synthesize fluid simulations
from a set of reduced parameters. A convolutional neural network is trained on
a collection of discrete, parameterizable fluid simulation velocity fields. Due
to the capability of deep learning architectures to learn representative
features of the data, our generative model is able to accurately approximate
the training data set, while providing plausible interpolated in-betweens. The
proposed generative model is optimized for fluids by a novel loss function that
guarantees divergence-free velocity fields at all times. In addition, we
demonstrate that we can handle complex parameterizations in reduced spaces, and
advance simulations in time by integrating in the latent space with a second
network. Our method models a wide variety of fluid behaviors, thus enabling
applications such as fast construction of simulations, interpolation of fluids
with different parameters, time re-sampling, latent space simulations, and
compression of fluid simulation data. Reconstructed velocity fields are
generated up to 700x faster than re-simulating the data with the underlying CPU
solver, while achieving compression rates of up to 1300x.Comment: Computer Graphics Forum (Proceedings of EUROGRAPHICS 2019),
additional materials: http://www.byungsoo.me/project/deep-fluids
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