32,340 research outputs found
Estimating the Spectrum in Computed Tomography Via Kullback–Leibler Divergence Constrained Optimization
Purpose
We study the problem of spectrum estimation from transmission data of a known phantom. The goal is to reconstruct an x‐ray spectrum that can accurately model the x‐ray transmission curves and reflects a realistic shape of the typical energy spectra of the CT system. Methods
Spectrum estimation is posed as an optimization problem with x‐ray spectrum as unknown variables, and a Kullback–Leibler (KL)‐divergence constraint is employed to incorporate prior knowledge of the spectrum and enhance numerical stability of the estimation process. The formulated constrained optimization problem is convex and can be solved efficiently by use of the exponentiated‐gradient (EG) algorithm. We demonstrate the effectiveness of the proposed approach on the simulated and experimental data. The comparison to the expectation–maximization (EM) method is also discussed. Results
In simulations, the proposed algorithm is seen to yield x‐ray spectra that closely match the ground truth and represent the attenuation process of x‐ray photons in materials, both included and not included in the estimation process. In experiments, the calculated transmission curve is in good agreement with the measured transmission curve, and the estimated spectra exhibits physically realistic looking shapes. The results further show the comparable performance between the proposed optimization‐based approach and EM. Conclusions
Our formulation of a constrained optimization provides an interpretable and flexible framework for spectrum estimation. Moreover, a KL‐divergence constraint can include a prior spectrum and appears to capture important features of x‐ray spectrum, allowing accurate and robust estimation of x‐ray spectrum in CT imaging
Measurability of kinetic temperature from metal absorption-line spectra formed in chaotic media
We present a new method for recovering the kinetic temperature of the
intervening diffuse gas to an accuracy of 10%. The method is based on the
comparison of unsaturated absorption-line profiles of two species with
different atomic weights. The species are assumed to have the same temperature
and bulk motion within the absorbing region. The computational technique
involves the Fourier transform of the absorption profiles and the consequent
Entropy-Regularized chi^2-Minimization [ERM] to estimate the model parameters.
The procedure is tested using synthetic spectra of CII, SiII and FeII ions. The
comparison with the standard Voigt fitting analysis is performed and it is
shown that the Voigt deconvolution of the complex absorption-line profiles may
result in estimated temperatures which are not physical. We also successfully
analyze Keck telescope spectra of CII1334 and SiII1260 lines observed at the
redshift z = 3.572 toward the quasar Q1937--1009 by Tytler {\it et al.}.Comment: 25 pages, 6 Postscript figures, aaspp4.sty file, submit. Ap
Component separation methods for the Planck mission
The Planck satellite will map the full sky at nine frequencies from 30 to 857
GHz. The CMB intensity and polarization that are its prime targets are
contaminated by foreground emission. The goal of this paper is to compare
proposed methods for separating CMB from foregrounds based on their different
spectral and spatial characteristics, and to separate the foregrounds into
components of different physical origin. A component separation challenge has
been organized, based on a set of realistically complex simulations of sky
emission. Several methods including those based on internal template
subtraction, maximum entropy method, parametric method, spatial and harmonic
cross correlation methods, and independent component analysis have been tested.
Different methods proved to be effective in cleaning the CMB maps from
foreground contamination, in reconstructing maps of diffuse Galactic emissions,
and in detecting point sources and thermal Sunyaev-Zeldovich signals. The power
spectrum of the residuals is, on the largest scales, four orders of magnitude
lower than that of the input Galaxy power spectrum at the foreground minimum.
The CMB power spectrum was accurately recovered up to the sixth acoustic peak.
The point source detection limit reaches 100 mJy, and about 2300 clusters are
detected via the thermal SZ effect on two thirds of the sky. We have found that
no single method performs best for all scientific objectives. We foresee that
the final component separation pipeline for Planck will involve a combination
of methods and iterations between processing steps targeted at different
objectives such as diffuse component separation, spectral estimation and
compact source extraction.Comment: Matches version accepted by A&A. A version with high resolution
figures is available at http://people.sissa.it/~leach/compsepcomp.pd
Likelihood Analysis of Power Spectra and Generalized Moment Problems
We develop an approach to spectral estimation that has been advocated by
Ferrante, Masiero and Pavon and, in the context of the scalar-valued covariance
extension problem, by Enqvist and Karlsson. The aim is to determine the power
spectrum that is consistent with given moments and minimizes the relative
entropy between the probability law of the underlying Gaussian stochastic
process to that of a prior. The approach is analogous to the framework of
earlier work by Byrnes, Georgiou and Lindquist and can also be viewed as a
generalization of the classical work by Burg and Jaynes on the maximum entropy
method. In the present paper we present a new fast algorithm in the general
case (i.e., for general Gaussian priors) and show that for priors with a
specific structure the solution can be given in closed form.Comment: 17 pages, 4 figure
Spectral Norm Regularization for Improving the Generalizability of Deep Learning
We investigate the generalizability of deep learning based on the sensitivity
to input perturbation. We hypothesize that the high sensitivity to the
perturbation of data degrades the performance on it. To reduce the sensitivity
to perturbation, we propose a simple and effective regularization method,
referred to as spectral norm regularization, which penalizes the high spectral
norm of weight matrices in neural networks. We provide supportive evidence for
the abovementioned hypothesis by experimentally confirming that the models
trained using spectral norm regularization exhibit better generalizability than
other baseline methods
Foreground separation using a flexible maximum-entropy algorithm: an application to COBE data
A flexible maximum-entropy component separation algorithm is presented that
accommodates anisotropic noise, incomplete sky-coverage and uncertainties in
the spectral parameters of foregrounds. The capabilities of the method are
determined by first applying it to simulated spherical microwave data sets
emulating the COBE-DMR, COBE-DIRBE and Haslam surveys. Using these simulations
we find that is very difficult to determine unambiguously the spectral
parameters of the galactic components for this data set due to their high level
of noise. Nevertheless, we show that is possible to find a robust CMB
reconstruction, especially at the high galactic latitude. The method is then
applied to these real data sets to obtain reconstructions of the CMB component
and galactic foreground emission over the whole sky. The best reconstructions
are found for values of the spectral parameters: T_d=19 K, alpha_d=2,
beta_ff=-0.19 and beta_syn=-0.8. The CMB map has been recovered with an
estimated statistical error of \sim 22 muK on an angular scale of 7 degrees
outside the galactic cut whereas the low galactic latitude region presents
contamination from the foreground emissions.Comment: 29 pages, 25 figures, version accepted for publication in MNRAS. One
subsection and 6 figures added. Main results unchange
Minimum Relative Entropy for Quantum Estimation: Feasibility and General Solution
We propose a general framework for solving quantum state estimation problems
using the minimum relative entropy criterion. A convex optimization approach
allows us to decide the feasibility of the problem given the data and, whenever
necessary, to relax the constraints in order to allow for a physically
admissible solution. Building on these results, the variational analysis can be
completed ensuring existence and uniqueness of the optimum. The latter can then
be computed by standard, efficient standard algorithms for convex optimization,
without resorting to approximate methods or restrictive assumptions on its
rank.Comment: 9 pages, no figure
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