6,088 research outputs found
Hierarchical Bayesian Detection Algorithm for Early-Universe Relics in the Cosmic Microwave Background
A number of theoretically well-motivated additions to the standard
cosmological model predict weak signatures in the form of spatially localized
sources embedded in the cosmic microwave background (CMB) fluctuations. We
present a hierarchical Bayesian statistical formalism and a complete data
analysis pipeline for testing such scenarios. We derive an accurate
approximation to the full posterior probability distribution over the
parameters defining any theory that predicts sources embedded in the CMB, and
perform an extensive set of tests in order to establish its validity. The
approximation is implemented using a modular algorithm, designed to avoid a
posteriori selection effects, which combines a candidate-detection stage with a
full Bayesian model-selection and parameter-estimation analysis. We apply this
pipeline to theories that predict cosmic textures and bubble collisions,
extending previous analyses by using: (1) adaptive-resolution techniques,
allowing us to probe features of arbitrary size, and (2) optimal filters, which
provide the best possible sensitivity for detecting candidate signatures. We
conclude that the WMAP 7-year data do not favor the addition of either cosmic
textures or bubble collisions to the standard cosmological model, and place
robust constraints on the predicted number of such sources. The expected
numbers of bubble collisions and cosmic textures on the CMB sky within our
detection thresholds are constrained to be fewer than 4.0 and 5.2 at 95%
confidence, respectively.Comment: 34 pages, 18 figures. v3: corrected very minor typos to match
published versio
Seven-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Are There Cosmic Microwave Background Anomalies?
(Abridged) A simple six-parameter LCDM model provides a successful fit to
WMAP data, both when the data are analyzed alone and in combination with other
cosmological data. Even so, it is appropriate to search for any hints of
deviations from the now standard model of cosmology, which includes inflation,
dark energy, dark matter, baryons, and neutrinos. The cosmological community
has subjected the WMAP data to extensive and varied analyses. While there is
widespread agreement as to the overall success of the six-parameter LCDM model,
various "anomalies" have been reported relative to that model. In this paper we
examine potential anomalies and present analyses and assessments of their
significance. In most cases we find that claimed anomalies depend on posterior
selection of some aspect or subset of the data. Compared with sky simulations
based on the best fit model, one can select for low probability features of the
WMAP data. Low probability features are expected, but it is not usually
straightforward to determine whether any particular low probability feature is
the result of the a posteriori selection or of non-standard cosmology. We
examine in detail the properties of the power spectrum with respect to the LCDM
model. We examine several potential or previously claimed anomalies in the sky
maps and power spectra, including cold spots, low quadrupole power,
quadropole-octupole alignment, hemispherical or dipole power asymmetry, and
quadrupole power asymmetry. We conclude that there is no compelling evidence
for deviations from the LCDM model, which is generally an acceptable
statistical fit to WMAP and other cosmological data.Comment: 19 pages, 17 figures, also available with higher-res figures on
http://lambda.gsfc.nasa.gov; accepted by ApJS; (v2) text as accepte
Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis
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
AWAIC: A WISE Astronomical Image Co-adder
We describe a new image co-addition tool, AWAIC, to support the creation of a
digital Image Atlas from the multiple frame exposures acquired with the
Wide-field Infrared Survey Explorer (WISE). AWAIC includes preparatory steps
such as frame background matching and outlier detection using robust
frame-stack statistics. Frame co-addition is based on using the detector's
Point Response Function (PRF) as an interpolation kernel. This kernel reduces
the impact of prior-masked pixels; enables the creation of an optimal matched
filtered product for point source detection; and most important, it allows for
resolution enhancement (HiRes) to yield a model of the sky that is consistent
with the observations to within measurement error. The HiRes functionality
allows for non-isoplanatic PRFs, prior noise-variance weighting, uncertainty
estimation, and includes a ringing-suppression algorithm. AWAIC also supports
the popular overlap-area weighted interpolation method, and is generic enough
for use on any astronomical image data that supports the FITS and WCS
standards.Comment: 16 pages, 6 figures. Invited paper to appear in Proceedings of ADASS
XVIII Conferenc
Constraining the redshift evolution of the Cosmic Microwave Background black-body temperature with PLANCK data
We constrain the deviation of adiabatic evolution of the Universe using the
data on the Cosmic Microwave Background (CMB) temperature anisotropies measured
by the {\it Planck} satellite and a sample of 481 X-ray selected clusters with
spectroscopically measured redshifts. To avoid antenna beam effects, we bring
all the maps to the same resolution. We use a CMB template to subtract the
cosmological signal while preserving the Thermal Sunyaev-Zeldovich (TSZ)
anisotropies; next, we remove galactic foreground emissions around each cluster
and we mask out all known point sources. If the CMB black-body temperature
scales with redshift as , we constrain deviations of
adiabatic evolution to be , consistent with the
temperature-redshift relation of the standard cosmological model. This result
could suffer from a potential bias associated with the CMB
template, that we quantify it to be and with the same
sign than the measured value of , but is free from those biases
associated with using TSZ selected clusters; it represents the best constraint
to date of the temperature-redshift relation of the Big-Bang model using only
CMB data, confirming previous results.Comment: ApJ, in press. Manuscript matches the accepted version: 10 pages, 7
figures, 3 table
The IPAC Image Subtraction and Discovery Pipeline for the intermediate Palomar Transient Factory
We describe the near real-time transient-source discovery engine for the
intermediate Palomar Transient Factory (iPTF), currently in operations at the
Infrared Processing and Analysis Center (IPAC), Caltech. We coin this system
the IPAC/iPTF Discovery Engine (or IDE). We review the algorithms used for
PSF-matching, image subtraction, detection, photometry, and machine-learned
(ML) vetting of extracted transient candidates. We also review the performance
of our ML classifier. For a limiting signal-to-noise ratio of 4 in relatively
unconfused regions, "bogus" candidates from processing artifacts and imperfect
image subtractions outnumber real transients by ~ 10:1. This can be
considerably higher for image data with inaccurate astrometric and/or
PSF-matching solutions. Despite this occasionally high contamination rate, the
ML classifier is able to identify real transients with an efficiency (or
completeness) of ~ 97% for a maximum tolerable false-positive rate of 1% when
classifying raw candidates. All subtraction-image metrics, source features, ML
probability-based real-bogus scores, contextual metadata from other surveys,
and possible associations with known Solar System objects are stored in a
relational database for retrieval by the various science working groups. We
review our efforts in mitigating false-positives and our experience in
optimizing the overall system in response to the multitude of science projects
underway with iPTF.Comment: 66 pages, 21 figures, 7 tables, accepted by PAS
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