785 research outputs found
Compressed sensing imaging techniques for radio interferometry
Radio interferometry probes astrophysical signals through incomplete and
noisy Fourier measurements. The theory of compressed sensing demonstrates that
such measurements may actually suffice for accurate reconstruction of sparse or
compressible signals. We propose new generic imaging techniques based on convex
optimization for global minimization problems defined in this context. The
versatility of the framework notably allows introduction of specific prior
information on the signals, which offers the possibility of significant
improvements of reconstruction relative to the standard local matching pursuit
algorithm CLEAN used in radio astronomy. We illustrate the potential of the
approach by studying reconstruction performances on simulations of two
different kinds of signals observed with very generic interferometric
configurations. The first kind is an intensity field of compact astrophysical
objects. The second kind is the imprint of cosmic strings in the temperature
field of the cosmic microwave background radiation, of particular interest for
cosmology.Comment: 10 pages, 1 figure. Version 2 matches version accepted for
publication in MNRAS. Changes includes: writing corrections, clarifications
of arguments, figure update, and a new subsection 4.1 commenting on the exact
compliance of radio interferometric measurements with compressed sensin
Compressed sensing imaging techniques for radio interferometry
Radio interferometry probes astrophysical signals through incomplete and noisy Fourier measurements. The theory of compressed sensing demonstrates that such measurements may actually suffice for accurate reconstruction of sparse or compressible signals. We propose new generic imaging techniques based on convex optimization for global minimization problems defined in this context. The versatility of the framework notably allows introduction of specific prior information on the signals, which offers the possibility of significant improvements of reconstruction relative to the standard local matching pursuit algorithm CLEAN used in radio astronomy. We illustrate the potential of the approach by studying reconstruction performances on simulations of two different kinds of signals observed with very generic interferometric configurations. The first kind is an intensity field of compact astrophysical objects. The second kind is the imprint of cosmic strings in the temperature field of the cosmic microwave background radiation, of particular interest for cosmolog
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
This paper focuses on an examination of an applicability of Recurrent Neural
Network models for detecting anomalous behavior of the CERN superconducting
magnets. In order to conduct the experiments, the authors designed and
implemented an adaptive signal quantization algorithm and a custom GRU-based
detector and developed a method for the detector parameters selection. Three
different datasets were used for testing the detector. Two artificially
generated datasets were used to assess the raw performance of the system
whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets
was intended for real-life experiments and model training. Several different
setups of the developed anomaly detection system were evaluated and compared
with state-of-the-art OC-SVM reference model operating on the same data. The
OC-SVM model was equipped with a rich set of feature extractors accounting for
a range of the input signal properties. It was determined in the course of the
experiments that the detector, along with its supporting design methodology,
reaches F1 equal or very close to 1 for almost all test sets. Due to the
profile of the data, the best_length setup of the detector turned out to
perform the best among all five tested configuration schemes of the detection
system. The quantization parameters have the biggest impact on the overall
performance of the detector with the best values of input/output grid equal to
16 and 8, respectively. The proposed solution of the detection significantly
outperformed OC-SVM-based detector in most of the cases, with much more stable
performance across all the datasets.Comment: Related to arXiv:1702.0083
Quantitative uniform in time chaos propagation for Boltzmann collision processes
This paper is devoted to the study of mean-field limit for systems of
indistinguables particles undergoing collision processes. As formulated by Kac
\cite{Kac1956} this limit is based on the {\em chaos propagation}, and we (1)
prove and quantify this property for Boltzmann collision processes with
unbounded collision rates (hard spheres or long-range interactions), (2) prove
and quantify this property \emph{uniformly in time}. This yields the first
chaos propagation result for the spatially homogeneous Boltzmann equation for
true (without cut-off) Maxwell molecules whose "Master equation" shares
similarities with the one of a L\'evy process and the first {\em quantitative}
chaos propagation result for the spatially homogeneous Boltzmann equation for
hard spheres (improvement of the %non-contructive convergence result of
Sznitman \cite{S1}). Moreover our chaos propagation results are the first
uniform in time ones for Boltzmann collision processes (to our knowledge),
which partly answers the important question raised by Kac of relating the
long-time behavior of a particle system with the one of its mean-field limit,
and we provide as a surprising application a new proof of the well-known result
of gaussian limit of rescaled marginals of uniform measure on the
-dimensional sphere as goes to infinity (more applications will be
provided in a forthcoming work). Our results are based on a new method which
reduces the question of chaos propagation to the one of proving a purely
functional estimate on some generator operators ({\em consistency estimate})
together with fine stability estimates on the flow of the limiting non-linear
equation ({\em stability estimates})
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