5,644 research outputs found
Process chain approach to high-order perturbation calculus for quantum lattice models
A method based on Rayleigh-Schroedinger perturbation theory is developed that
allows to obtain high-order series expansions for ground-state properties of
quantum lattice models. The approach is capable of treating both lattice
geometries of large spatial dimensionalities d and on-site degrees of freedom
with large state space dimensionalities. It has recently been used to
accurately compute the zero-temperature phase diagram of the Bose-Hubbard model
on a hypercubic lattice, up to arbitrary large filling and for d=2, 3 and
greater [Teichmann et al., Phys. Rev. B 79, 100503(R) (2009)].Comment: 11 pages, 6 figure
An Algorithmic Approach to Quantum Field Theory
The lattice formulation provides a way to regularize, define and compute the
Path Integral in a Quantum Field Theory. In this paper we review the
theoretical foundations and the most basic algorithms required to implement a
typical lattice computation, including the Metropolis, the Gibbs sampling, the
Minimal Residual, and the Stabilized Biconjugate inverters. The main emphasis
is on gauge theories with fermions such as QCD. We also provide examples of
typical results from lattice QCD computations for quantities of
phenomenological interest.Comment: 44 pages, to be published in IJMP
Neural Network Aided Glitch-Burst Discrimination and Glitch Classification
We investigate the potential of neural-network based classifiers for
discriminating gravitational wave bursts (GWBs) of a given canonical family
(e.g. core-collapse supernova waveforms) from typical transient instrumental
artifacts (glitches), in the data of a single detector. The further
classification of glitches into typical sets is explored.In order to provide a
proof of concept,we use the core-collapse supernova waveform catalog produced
by H. Dimmelmeier and co-Workers, and the data base of glitches observed in
laser interferometer gravitational wave observatory (LIGO) data maintained by
P. Saulson and co-Workers to construct datasets of (windowed) transient
waveforms (glitches and bursts) in additive (Gaussian and compound-Gaussian)
noise with different signal-tonoise ratios (SNR). Principal component analysis
(PCA) is next implemented for reducing data dimensionality, yielding results
consistent with, and extending those in the literature. Then, a multilayer
perceptron is trained by a backpropagation algorithm (MLP-BP) on a data subset,
and used to classify the transients as glitch or burst. A Self-Organizing Map
(SOM) architecture is finally used to classify the glitches. The glitch/burst
discrimination and glitch classification abilities are gauged in terms of the
related truth tables. Preliminary results suggest that the approach is
effective and robust throughout the SNR range of practical interest.
Perspective applications pertain both to distributed (network, multisensor)
detection of GWBs, where someintelligenceat the single node level can be
introduced, and instrument diagnostics/optimization, where spurious transients
can be identified, classified and hopefully traced back to their entry point
New Phases of SU(3) and SU(4) at Finite Temperature
The addition of an adjoint Polyakov loop term to the action of a pure gauge
theory at finite temperature leads to new phases of SU(N) gauge theories. For
SU(3), a new phase is found which breaks Z(3) symmetry in a novel way; for
SU(4), the new phase exhibits spontaneous symmetry breaking of Z(4) to Z(2),
representing a partially confined phase in which quarks are confined, but
diquarks are not. The overall phase structure and thermodynamics is consistent
with a theoretical model of the effective potential for the Polyakov loop based
on perturbation theory.Comment: 18 pages, 17 figures, RevTeX
Quantifying Timing Leaks and Cost Optimisation
We develop a new notion of security against timing attacks where the attacker
is able to simultaneously observe the execution time of a program and the
probability of the values of low variables. We then show how to measure the
security of a program with respect to this notion via a computable estimate of
the timing leakage and use this estimate for cost optimisation.Comment: 16 pages, 2 figures, 4 tables. A shorter version is included in the
proceedings of ICICS'08 - 10th International Conference on Information and
Communications Security, 20-22 October, 2008 Birmingham, U
Tanaka-Tagoshi Parametrization of post-1PN Spin-Free Gravitational Wave Chirps: Equispaced and Cardinal Interpolated Lattices For First Generation Interferometric Antennas
The spin-free binary-inspiral parameter-space introduced by Tanaka and
Tagoshi to construct a uniformly-spaced lattice of templates at (and possibly
beyond) order is shown to work for all first generation interferometric
gravitational wave antennas. This allows to extend the minimum-redundant
cardinal interpolation techniques of the correlator bank developed by the
Authors to the highest available order PN templates. The total number of 2PN
templates to be computed for a minimal match is reduced by a
factor 4, as in the 1PN case.Comment: 9 pages, 8 figures, 3 tables, accepted for publication in Phys. Rev.
Probabilistic abstract interpretation: From trace semantics to DTMC’s and linear regression
In order to perform probabilistic program analysis we need to consider probabilistic languages or languages with a probabilistic semantics, as well as a corresponding framework for the analysis which is able to accommodate probabilistic properties and properties of probabilistic computations. To this purpose we investigate the relationship between three different types of probabilistic semantics for a core imperative language, namely Kozen’s Fixpoint Semantics, our Linear Operator Semantics and probabilistic versions of Maximal Trace Semantics. We also discuss the relationship between Probabilistic Abstract Interpretation (PAI) and statistical or linear regression analysis. While classical Abstract Interpretation, based on Galois connection, allows only for worst-case analyses, the use of the Moore-Penrose pseudo inverse in PAI opens the possibility of exploiting statistical and noisy observations in order to analyse and identify various system properties
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