1,878 research outputs found
A Markov Chain Monte Carlo Algorithm for analysis of low signal-to-noise CMB data
We present a new Monte Carlo Markov Chain algorithm for CMB analysis in the
low signal-to-noise regime. This method builds on and complements the
previously described CMB Gibbs sampler, and effectively solves the low
signal-to-noise inefficiency problem of the direct Gibbs sampler. The new
algorithm is a simple Metropolis-Hastings sampler with a general proposal rule
for the power spectrum, C_l, followed by a particular deterministic rescaling
operation of the sky signal. The acceptance probability for this joint move
depends on the sky map only through the difference of chi-squared between the
original and proposed sky sample, which is close to unity in the low
signal-to-noise regime. The algorithm is completed by alternating this move
with a standard Gibbs move. Together, these two proposals constitute a
computationally efficient algorithm for mapping out the full joint CMB
posterior, both in the high and low signal-to-noise regimes.Comment: Submitted to Ap
Researcher's guide to the NASA Ames Flight Simulator for Advanced Aircraft (FSAA)
Performance, limitations, supporting software, and current checkout and operating procedures are presented for the flight simulator, in terms useful to the researcher who intends to use it. Suggestions to help the researcher prepare the experimental plan are also given. The FSAA's central computer, cockpit, and visual and motion systems are addressed individually but their interaction is considered as well. Data required, available options, user responsibilities, and occupancy procedures are given in a form that facilitates the initial communication required with the NASA operations' group
Bayesian analysis of the low-resolution polarized 3-year WMAP sky maps
We apply a previously developed Gibbs sampling framework to the foreground
corrected 3-yr WMAP polarization data and compute the power spectrum and
residual foreground template amplitude posterior distributions. We first
analyze the co-added Q- and V-band data, and compare our results to the
likelihood code published by the WMAP team. We find good agreement, and thus
verify the numerics and data processing steps of both approaches. However, we
also analyze the Q- and V-bands separately, allowing for non-zero EB
cross-correlations and including two individual foreground template amplitudes
tracing synchrotron and dust emission. In these analyses, we find tentative
evidence of systematics: The foreground tracers correlate with each of the Q-
and V-band sky maps individually, although not with the co-added QV map; there
is a noticeable negative EB cross-correlation at l <~ 16 in the V-band map; and
finally, when relaxing the constraints on EB and BB, noticeable differences are
observed between the marginalized band powers in the Q- and V-bands. Further
studies of these features are imperative, given the importance of the low-l EE
spectrum on the optical depth of reionization tau and the spectral index of
scalar perturbations n_s.Comment: 5 pages, 4 figures, submitted to ApJ
Application of Monte Carlo Algorithms to the Bayesian Analysis of the Cosmic Microwave Background
Power spectrum estimation and evaluation of associated errors in the presence
of incomplete sky coverage; non-homogeneous, correlated instrumental noise; and
foreground emission is a problem of central importance for the extraction of
cosmological information from the cosmic microwave background. We develop a
Monte Carlo approach for the maximum likelihood estimation of the power
spectrum. The method is based on an identity for the Bayesian posterior as a
marginalization over unknowns. Maximization of the posterior involves the
computation of expectation values as a sample average from maps of the cosmic
microwave background and foregrounds given some current estimate of the power
spectrum or cosmological model, and some assumed statistical characterization
of the foregrounds. Maps of the CMB are sampled by a linear transform of a
Gaussian white noise process, implemented numerically with conjugate gradient
descent. For time series data with N_{t} samples, and N pixels on the sphere,
the method has a computational expense $KO[N^{2} +- N_{t} +AFw-log N_{t}],
where K is a prefactor determined by the convergence rate of conjugate gradient
descent. Preconditioners for conjugate gradient descent are given for scans
close to great circle paths, and the method allows partial sky coverage for
these cases by numerically marginalizing over the unobserved, or removed,
region.Comment: submitted to Ap
The joint large-scale foreground-CMB posteriors of the 3-year WMAP data
Using a Gibbs sampling algorithm for joint CMB estimation and component
separation, we compute the large-scale CMB and foreground posteriors of the
3-yr WMAP temperature data. Our parametric data model includes the cosmological
CMB signal and instrumental noise, a single power law foreground component with
free amplitude and spectral index for each pixel, a thermal dust template with
a single free overall amplitude, and free monopoles and dipoles at each
frequency. This simple model yields a surprisingly good fit to the data over
the full frequency range from 23 to 94 GHz. We obtain a new estimate of the CMB
sky signal and power spectrum, and a new foreground model, including a
measurement of the effective spectral index over the high-latitude sky. A
particularly significant result is the detection of a common spurious offset in
all frequency bands of ~ -13muK, as well as a dipole in the V-band data.
Correcting for these is essential when determining the effective spectral index
of the foregrounds. We find that our new foreground model is in good agreement
with template-based model presented by the WMAP team, but not with their MEM
reconstruction. We believe the latter may be at least partially compromised by
the residual offsets and dipoles in the data. Fortunately, the CMB power
spectrum is not significantly affected by these issues, as our new spectrum is
in excellent agreement with that published by the WMAP team. The corresponding
cosmological parameters are also virtually unchanged.Comment: 5 pages, 4 figures, submitted to ApJL. Background data are available
at http://www.astro.uio.no/~hke under the Research ta
Bayesian Power Spectrum Analysis of the First-Year WMAP data
We present the first results from a Bayesian analysis of the WMAP first year
data using a Gibbs sampling technique. Using two independent, parallel
supercomputer codes we analyze the WMAP Q, V and W bands. The analysis results
in a full probabilistic description of the information the WMAP data set
contains about the power spectrum and the all-sky map of the cosmic microwave
background anisotropies. We present the complete probability distributions for
each C_l including any non-Gaussianities of the power spectrum likelihood.
While we find good overall agreement with the previously published WMAP
spectrum, our analysis uncovers discrepancies in the power spectrum estimates
at low l multipoles. For example we claim the best-fit Lambda-CDM model is
consistent with the C_2 inferred from our combined Q+V+W analysis with a 10%
probability of an even larger theoretical C_2. Based on our exact analysis we
can therefore attribute the "low quadrupole issue" to a statistical
fluctuation.Comment: 5 pages. 4 figures. For additional information and data see
http://www.astro.uiuc.edu/~iodwyer/research#wma
CMB likelihood approximation by a Gaussianized Blackwell-Rao estimator
We introduce a new CMB temperature likelihood approximation called the
Gaussianized Blackwell-Rao (GBR) estimator. This estimator is derived by
transforming the observed marginal power spectrum distributions obtained by the
CMB Gibbs sampler into standard univariate Gaussians, and then approximate
their joint transformed distribution by a multivariate Gaussian. The method is
exact for full-sky coverage and uniform noise, and an excellent approximation
for sky cuts and scanning patterns relevant for modern satellite experiments
such as WMAP and Planck. A single evaluation of this estimator between l=2 and
200 takes ~0.2 CPU milliseconds, while for comparison, a single pixel space
likelihood evaluation between l=2 and 30 for a map with ~2500 pixels requires
~20 seconds. We apply this tool to the 5-year WMAP temperature data, and
re-estimate the angular temperature power spectrum, , and likelihood,
L(C_l), for l<=200, and derive new cosmological parameters for the standard
six-parameter LambdaCDM model. Our spectrum is in excellent agreement with the
official WMAP spectrum, but we find slight differences in the derived
cosmological parameters. Most importantly, the spectral index of scalar
perturbations is n_s=0.973 +/- 0.014, 1.9 sigma away from unity and 0.6 sigma
higher than the official WMAP result, n_s = 0.965 +/- 0.014. This suggests that
an exact likelihood treatment is required to higher l's than previously
believed, reinforcing and extending our conclusions from the 3-year WMAP
analysis. In that case, we found that the sub-optimal likelihood approximation
adopted between l=12 and 30 by the WMAP team biased n_s low by 0.4 sigma, while
here we find that the same approximation between l=30 and 200 introduces a bias
of 0.6 sigma in n_s.Comment: 10 pages, 7 figures, submitted to Ap
Optical imaging of the effect of in-plane fields on cholesteric liquid crystals
Sharon A. Jewell and J. Roy Sambles, Physical Review E, Vol. 78, article 012701 (2008). Copyright © 2008 by the American Physical Society.The effects of in-plane electric fields on the director structure of cholesteric liquid crystals has been imaged in three dimensions using fluorescence confocal polarizing microscopy. The results show that a liquid crystal lying outside the electrode gap can be significantly affected by stray fields occurring above the electrode surface, resulting in a 90° rotation of the cholesteric helix. Distinct differences between the behavior of cholesterics with positive and negative dielectric anisotropies are observed
Fluid Mechanics of Everyday Objects
High speed Schlieren videos were produced highlighting the fluid mechanics
found in everyday objects. This video (entry 102369) was submitted as part of
the Gallery of Fluid Motion 2013, which is a showcase of fluid dynamics videos.Comment: Both a high-resolution version and a low-resolution version of the
submitted video are available for downloa
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