245,648 research outputs found
Report on the first round of the Mock LISA Data Challenges
The Mock LISA Data Challenges (MLDCs) have the dual purpose of fostering the
development of LISA data analysis tools and capabilities, and demonstrating the
technical readiness already achieved by the gravitational-wave community in
distilling a rich science payoff from the LISA data output. The first round of
MLDCs has just been completed: nine data sets containing simulated
gravitational wave signals produced either by galactic binaries or massive
black hole binaries embedded in simulated LISA instrumental noise were released
in June 2006 with deadline for submission of results at the beginning of
December 2006. Ten groups have participated in this first round of challenges.
Here we describe the challenges, summarise the results, and provide a first
critical assessment of the entries.Comment: Proceedings report from GWDAW 11. Added author, added reference,
clarified some text, removed typos. Results unchanged; Removed author, minor
edits, reflects submitted versio
Report on the first round of the Mock LISA Data Challenges
The Mock LISA Data Challenges (MLDCs) have the dual purpose of fostering the development of LISA data analysis tools and capabilities, and demonstrating the technical readiness already achieved by the gravitational-wave community in distilling a rich science payoff from the LISA data output. The first round of MLDCs has just been completed: nine challenges consisting of data sets containing simulated gravitational-wave signals produced either by galactic binaries or massive black hole binaries embedded in simulated LISA instrumental noise were released in June 2006 with deadline for submission of results at the beginning of December 2006. Ten groups have participated in this first round of challenges. All of the challenges had at least one entry which successfully characterized the signal to better than 95% when assessed via a correlation with phasing ambiguities accounted for. Here, we describe the challenges, summarize the results and provide a first critical assessment of the entries
A simple and objective method for reproducible resting state network (RSN) detection in fMRI
Spatial Independent Component Analysis (ICA) decomposes the time by space
functional MRI (fMRI) matrix into a set of 1-D basis time courses and their
associated 3-D spatial maps that are optimized for mutual independence. When
applied to resting state fMRI (rsfMRI), ICA produces several spatial
independent components (ICs) that seem to have biological relevance - the
so-called resting state networks (RSNs). The ICA problem is well posed when the
true data generating process follows a linear mixture of ICs model in terms of
the identifiability of the mixing matrix. However, the contrast function used
for promoting mutual independence in ICA is dependent on the finite amount of
observed data and is potentially non-convex with multiple local minima. Hence,
each run of ICA could produce potentially different IC estimates even for the
same data. One technique to deal with this run-to-run variability of ICA was
proposed by Yang et al. (2008) in their algorithm RAICAR which allows for the
selection of only those ICs that have a high run-to-run reproducibility. We
propose an enhancement to the original RAICAR algorithm that enables us to
assign reproducibility p-values to each IC and allows for an objective
assessment of both within subject and across subjects reproducibility. We call
the resulting algorithm RAICAR-N (N stands for null hypothesis test), and we
have applied it to publicly available human rsfMRI data (http://www.nitrc.org).
Our reproducibility analyses indicated that many of the published RSNs in
rsfMRI literature are highly reproducible. However, we found several other RSNs
that are highly reproducible but not frequently listed in the literature.Comment: 54 pages, 13 figure
Science Icebreaker Activities: An Example from Gravitational Wave Astronomy
At the beginning of a class or meeting an icebreaker activity is often used
to help loosen the group and get everyone talking. Our motivation is to develop
activities that serve the purpose of an icebreaker, but are designed to enhance
and supplement a science-oriented agenda. The subject of this article is an
icebreaker activity related to gravitational wave astronomy. We first describe
the unique gravitational wave signals from three distinct sources:
monochromatic binaries, merging compact objects, and extreme mass ratio
encounters. These signals form the basis of the activity where participants
work to match an ideal gravitational wave signal with noisy detector output for
each type of source.Comment: Accepted to The Physics Teacher. Original manuscript divided into two
papers at the request of the referee. For a related paper on gravitational
wave observatories see physics/050920
Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project
The Numerical INJection Analysis (NINJA) project is a collaborative effort
between members of the numerical relativity and gravitational-wave data
analysis communities. The purpose of NINJA is to study the sensitivity of
existing gravitational-wave search algorithms using numerically generated
waveforms and to foster closer collaboration between the numerical relativity
and data analysis communities. We describe the results of the first NINJA
analysis which focused on gravitational waveforms from binary black hole
coalescence. Ten numerical relativity groups contributed numerical data which
were used to generate a set of gravitational-wave signals. These signals were
injected into a simulated data set, designed to mimic the response of the
Initial LIGO and Virgo gravitational-wave detectors. Nine groups analysed this
data using search and parameter-estimation pipelines. Matched filter
algorithms, un-modelled-burst searches and Bayesian parameter-estimation and
model-selection algorithms were applied to the data. We report the efficiency
of these search methods in detecting the numerical waveforms and measuring
their parameters. We describe preliminary comparisons between the different
search methods and suggest improvements for future NINJA analyses.Comment: 56 pages, 25 figures; various clarifications; accepted to CQ
Time-frequency analysis of extreme-mass-ratio inspiral signals in mock LISA data
Extreme-mass-ratio inspirals (EMRIs) of ~ 1-10 solar-mass compact objects
into ~ million solar-mass massive black holes can serve as excellent probes of
strong-field general relativity. The Laser Interferometer Space Antenna (LISA)
is expected to detect gravitational wave signals from apprxomiately one hundred
EMRIs per year, but the data analysis of EMRI signals poses a unique set of
challenges due to their long duration and the extensive parameter space of
possible signals. One possible approach is to carry out a search for EMRI
tracks in the time-frequency domain. We have applied a time-frequency search to
the data from the Mock LISA Data Challenge (MLDC) with promising results. Our
analysis used the Hierarchical Algorithm for Clusters and Ridges to identify
tracks in the time-frequency spectrogram corresponding to EMRI sources. We then
estimated the EMRI source parameters from these tracks. In these proceedings,
we discuss the results of this analysis of the MLDC round 1.3 data.Comment: Amaldi-7 conference proceedings; requires jpconf style file
A First Comparison Between LIGO and Virgo Inspiral Search Pipelines
This article reports on a project that is the first step the LIGO Scientific
Collaboration and the Virgo Collaboration have taken to prepare for the mutual
search for inspiral signals. The project involved comparing the analysis
pipelines of the two collaborations on data sets prepared by both sides,
containing simulated noise and injected events. The ability of the pipelines to
detect the injected events was checked, and a first comparison of how the
parameters of the events were recovered has been completed.Comment: GWDAW-9 proceeding
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