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 $\mu$ required to normalize to the WMAP 3-year data at multipole $\ell = 10$ is $G\mu = [2.04\pm0.06\textrm{(stat.)}\pm0.12\textrm{(sys.)}] \times 10^{-6}$, where we have quoted statistical and systematic errors separately, and $G$ 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