165,399 research outputs found
Data analysis strategies for the detection of gravitational waves in non-Gaussian noise
In order to analyze data produced by the kilometer-scale gravitational wave
detectors that will begin operation early next century, one needs to develop
robust statistical tools capable of extracting weak signals from the detector
noise. This noise will likely have non-stationary and non-Gaussian components.
To facilitate the construction of robust detection techniques, I present a
simple two-component noise model that consists of a background of Gaussian
noise as well as stochastic noise bursts. The optimal detection statistic
obtained for such a noise model incorporates a natural veto which suppresses
spurious events that would be caused by the noise bursts. When two detectors
are present, I show that the optimal statistic for the non-Gaussian noise model
can be approximated by a simple coincidence detection strategy. For simulated
detector noise containing noise bursts, I compare the operating characteristics
of (i) a locally optimal detection statistic (which has nearly-optimal behavior
for small signal amplitudes) for the non-Gaussian noise model, (ii) a standard
coincidence-style detection strategy, and (iii) the optimal statistic for
Gaussian noise.Comment: 5 pages RevTeX, 4 figure
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
An information-theoretic approach to the gravitational-wave burst detection problem
The observational era of gravitational-wave astronomy began in the Fall of
2015 with the detection of GW150914. One potential type of detectable
gravitational wave is short-duration gravitational-wave bursts, whose waveforms
can be difficult to predict. We present the framework for a new detection
algorithm for such burst events -- \textit{oLIB} -- that can be used in
low-latency to identify gravitational-wave transients independently of other
search algorithms. This algorithm consists of 1) an excess-power event
generator based on the Q-transform -- \textit{Omicron} --, 2) coincidence of
these events across a detector network, and 3) an analysis of the coincident
events using a Markov chain Monte Carlo Bayesian evidence calculator --
\textit{LALInferenceBurst}. These steps compress the full data streams into a
set of Bayes factors for each event; through this process, we use elements from
information theory to minimize the amount of information regarding the
signal-versus-noise hypothesis that is lost. We optimally extract this
information using a likelihood-ratio test to estimate a detection significance
for each event. Using representative archival LIGO data, we show that the
algorithm can detect gravitational-wave burst events of astrophysical strength
in realistic instrumental noise across different burst waveform morphologies.
We also demonstrate that the combination of Bayes factors by means of a
likelihood-ratio test can improve the detection efficiency of a
gravitational-wave burst search. Finally, we show that oLIB's performance is
robust against the choice of gravitational-wave populations used to model the
likelihood-ratio test likelihoods
First Observational Tests of Eternal Inflation: Analysis Methods and WMAP 7-Year Results
In the picture of eternal inflation, our observable universe resides inside a
single bubble nucleated from an inflating false vacuum. Many of the theories
giving rise to eternal inflation predict that we have causal access to
collisions with other bubble universes, providing an opportunity to confront
these theories with observation. We present the results from the first
observational search for the effects of bubble collisions, using cosmic
microwave background data from the WMAP satellite. Our search targets a generic
set of properties associated with a bubble collision spacetime, which we
describe in detail. We use a modular algorithm that is designed to avoid a
posteriori selection effects, automatically picking out the most promising
signals, performing a search for causal boundaries, and conducting a full
Bayesian parameter estimation and model selection analysis. We outline each
component of this algorithm, describing its response to simulated CMB skies
with and without bubble collisions. Comparing the results for simulated bubble
collisions to the results from an analysis of the WMAP 7-year data, we rule out
bubble collisions over a range of parameter space. Our model selection results
based on WMAP 7-year data do not warrant augmenting LCDM with bubble
collisions. Data from the Planck satellite can be used to more definitively
test the bubble collision hypothesis.Comment: Companion to arXiv:1012.1995. 41 pages, 23 figures. v2: replaced with
version accepted by PRD. Significant extensions to the Bayesian pipeline to
do the full-sky non-Gaussian source detection problem (previously restricted
to patches). Note that this has changed the normalization of evidence values
reported previously, as full-sky priors are now employed, but the conclusions
remain unchange
- …