140,861 research outputs found
Cross correlation anomaly detection system
This invention provides a method for automatically inspecting the surface of an object, such as an integrated circuit chip, whereby the data obtained by the light reflected from the surface, caused by a scanning light beam, is automatically compared with data representing acceptable values for each unique surface. A signal output provided indicated of acceptance or rejection of the chip. Acceptance is based on predetermined statistical confidence intervals calculated from known good regions of the object being tested, or their representative values. The method can utilize a known good chip, a photographic mask from which the I.C. was fabricated, or a computer stored replica of each pattern being tested
Cross-correlation cosmography with HI intensity mapping
The cross-correlation of a foreground density field with two different
background convergence fields can be used to measure cosmographic distance
ratios and constrain dark energy parameters. We investigate the possibility of
performing such measurements using a combination of optical galaxy surveys and
HI intensity mapping surveys, with emphasis on the performance of the planned
Square Kilometre Array (SKA). Using HI intensity mapping to probe the
foreground density tracer field and/or the background source fields has the
advantage of excellent redshift resolution and a longer lever arm achieved by
using the lensing signal from high redshift background sources. Our results
show that, for our best SKA-optical configuration of surveys, a constant
equation of state for dark energy can be constrained to for a sky
coverage and assuming a prior
for the dark energy density parameter. We also show that using the CMB as the
second source plane is not competitive, even when considering a COrE-like
satellite.Comment: 10 pages, 8 figures, 1 table; version accepted for publication in
Physical Review
Multifractal detrending moving average cross-correlation analysis
There are a number of situations in which several signals are simultaneously
recorded in complex systems, which exhibit long-term power-law
cross-correlations. The multifractal detrended cross-correlation analysis
(MF-DCCA) approaches can be used to quantify such cross-correlations, such as
the MF-DCCA based on detrended fluctuation analysis (MF-X-DFA) method. We
develop in this work a class of MF-DCCA algorithms based on the detrending
moving average analysis, called MF-X-DMA. The performances of the MF-X-DMA
algorithms are compared with the MF-X-DFA method by extensive numerical
experiments on pairs of time series generated from bivariate fractional
Brownian motions, two-component autoregressive fractionally integrated moving
average processes and binomial measures, which have theoretical expressions of
the multifractal nature. In all cases, the scaling exponents extracted
from the MF-X-DMA and MF-X-DFA algorithms are very close to the theoretical
values. For bivariate fractional Brownian motions, the scaling exponent of the
cross-correlation is independent of the cross-correlation coefficient between
two time series and the MF-X-DFA and centered MF-X-DMA algorithms have
comparative performance, which outperform the forward and backward MF-X-DMA
algorithms. We apply these algorithms to the return time series of two stock
market indexes and to their volatilities. For the returns, the centered
MF-X-DMA algorithm gives the best estimates of since its
is closest to 0.5 as expected, and the MF-X-DFA algorithm has the
second best performance. For the volatilities, the forward and backward
MF-X-DMA algorithms give similar results, while the centered MF-X-DMA and the
MF-X-DFA algorithms fails to extract rational multifractal nature.Comment: 15 pages, 4 figures, 2 matlab codes for MF-X-DMA and MF-X-DF
A cross-correlation of WMAP and ROSAT
We cross-correlate the recent CMB WMAP 1 year data with the diffuse soft
X-ray background map of ROSAT. We look for common signatures due to galaxy
clusters (SZ effect in CMB, bremsstrahlung in X-rays) by cross-correlating the
two maps in real and in Fourier space. We do not find any significant
correlation and we explore the different reasons for this lack of correlation.
The most likely candidates are the possibility that we live in a low universe () and/or systematic effects in the data
especially in the diffuse X-ray maps which may suffer from significant cluster
signal subtraction during the point source removal process.Comment: To appear in New Astronomy Reviews, Proceedings of the CMBNET
Meeting, 20-21 February, 2003, Oxford, U
Cross-Correlation Studies with CMB Polarization Maps
The free-electron population during the reionized epoch rescatters CMB
temperature quadrupole and generates a now well-known polarization signal at
large angular scales. While this contribution has been detected in the
temperature-polarization cross power spectrum measured with WMAP data, due to
the large cosmic variance associated with anisotropy measurements at tens of
degree angular scales only limited information related to reionization, such as
the optical depth to electron scattering, can be extracted. The inhomogeneities
in the free-electron population lead to an additional secondary polarization
anisotropy contribution at arcminute scales. While the fluctuation amplitude,
relative to dominant primordial fluctuations, is small, we suggest that a
cross-correlation between arcminute scale CMB polarization data and a tracer
field of the high redshift universe, such as through fluctuations captured by
the 21 cm neutral Hydrogen background or those in the infrared background
related to first proto-galaxies, may allow one to study additional details
related to reionization. For this purpose, we discuss an optimized higher order
correlation measurement, in the form of a three-point function, including
information from large angular scale CMB temperature anisotropies in addition
to arcminute scale polarization signal related to inhomogeneous reionization.
We suggest that the proposed bispectrum can be measured with a substantial
signal-to-noise ratio and does not require all-sky maps of CMB polarization or
that of the tracer field. A measurement such as the one proposed may allow one
to establish the epoch when CMB polarization related to reionization is
generated and to address if the universe was reionized once or twice.Comment: 13 pages, 7 figures; Version in press with Phys. Rev.
Atmospheric Stellar Parameters from Cross-Correlation Functions
The increasing number of spectra gathered by spectroscopic sky surveys and
transiting exoplanet follow-up has pushed the community to develop automated
tools for atmospheric stellar parameters determination. Here we present a novel
approach that allows the measurement of temperature (),
metallicity () and gravity () within a few seconds
and in a completely automated fashion. Rather than performing comparisons with
spectral libraries, our technique is based on the determination of several
cross-correlation functions (CCFs) obtained by including spectral features with
different sensitivity to the photospheric parameters. We use literature stellar
parameters of high signal-to-noise (), high-resolution HARPS
spectra of FGK Main Sequence stars to calibrate , and as a function of CCFs parameters. Our technique is validated
using low spectra obtained with the same instrument. For FGK
stars we achieve a precision of K, and at , while the precision for observation with
and the overall accuracy are constrained by the
literature values used to calibrate the CCFs. Our approach can be easily
extended to other instruments with similar spectral range and resolution, or to
other spectral range and stars other than FGK dwarfs if a large sample of
reference stars is available for the calibration. Additionally, we provide the
mathematical formulation to convert synthetic equivalent widths to CCF
parameters as an alternative to direct calibration. We have made our tool
publicly available.Comment: Accepted by MNRAS. 12 pages, 12 figures. The code to retrieve the
atmospheric stellar parameters from HARPS and HARPS-N spectra is available
"at this url, https://github.com/LucaMalavolta/CCFpams
Cross-Correlation in cricket data and RMT
We analyze cross-correlation between runs scored over a time interval in
cricket matches of different teams using methods of random matrix theory (RMT).
We obtain an ensemble of cross-correlation matrices from runs scored by
eight cricket playing nations for (i) test cricket from 1877 -2014 (ii)one-day
internationals from 1971 -2014 and (iii) seven teams participating in the
Indian Premier league T20 format (2008-2014) respectively. We find that a
majority of the eigenvalues of C fall within the bounds of random matrices
having joint probability distribution where
and is the Dyson parameter. The corresponding level density gives
Marchenko-Pastur (MP) distribution while fluctuations of every participating
team agrees with the universal behavior of Gaussian Unitary Ensemble (GUE). We
analyze the components of the deviating eigenvalues and find that the largest
eigenvalue corresponds to an influence common to all matches played during
these periods.Comment: 12 pages, 6 figure
A Modified Cross Correlation Algorithm for Reference-free Image Alignment of Non-Circular Projections in Single-Particle Electron Microscopy
In this paper we propose a modified cross correlation method to align images
from the same class in single-particle electron microscopy of highly
non-spherical structures. In this new method, First we coarsely align
projection images, and then re-align the resulting images using the cross
correlation (CC) method. The coarse alignment is obtained by matching the
centers of mass and the principal axes of the images. The distribution of
misalignment in this coarse alignment can be quantified based on the
statistical properties of the additive background noise. As a consequence, the
search space for re-alignment in the cross correlation method can be reduced to
achieve better alignment. In order to overcome problems associated with false
peaks in the cross correlations function, we use artificially blurred images
for the early stage of the iterative cross correlation method and segment the
intermediate class average from every iteration step. These two additional
manipulations combined with the reduced search space size in the cross
correlation method yield better alignments for low signal-to-noise ratio images
than both classical cross correlation and maximum likelihood(ML) methods.Comment: 29page
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