746 research outputs found
A Bayesian approach to the follow-up of candidate gravitational wave signals
Ground-based gravitational wave laser interferometers (LIGO, GEO-600, Virgo
and Tama-300) have now reached high sensitivity and duty cycle. We present a
Bayesian evidence-based approach to the search for gravitational waves, in
particular aimed at the followup of candidate events generated by the analysis
pipeline. We introduce and demonstrate an efficient method to compute the
evidence and odds ratio between different models, and illustrate this approach
using the specific case of the gravitational wave signal generated during the
inspiral phase of binary systems, modelled at the leading quadrupole Newtonian
order, in synthetic noise. We show that the method is effective in detecting
signals at the detection threshold and it is robust against (some types of)
instrumental artefacts. The computational efficiency of this method makes it
scalable to the analysis of all the triggers generated by the analysis
pipelines to search for coalescing binaries in surveys with ground-based
interferometers, and to a whole variety of signal waveforms, characterised by a
larger number of parameters.Comment: 9 page
Studying stellar binary systems with the Laser Interferometer Space Antenna using Delayed Rejection Markov chain Monte Carlo methods
Bayesian analysis of LISA data sets based on Markov chain Monte Carlo methods
has been shown to be a challenging problem, in part due to the complicated
structure of the likelihood function consisting of several isolated local
maxima that dramatically reduces the efficiency of the sampling techniques.
Here we introduce a new fully Markovian algorithm, a Delayed Rejection
Metropolis-Hastings Markov chain Monte Carlo method, to efficiently explore
these kind of structures and we demonstrate its performance on selected LISA
data sets containing a known number of stellar-mass binary signals embedded in
Gaussian stationary noise.Comment: 12 pages, 4 figures, accepted in CQG (GWDAW-13 proceedings
Bayesian coherent analysis of in-spiral gravitational wave signals with a detector network
The present operation of the ground-based network of gravitational-wave laser
interferometers in "enhanced" configuration brings the search for gravitational
waves into a regime where detection is highly plausible. The development of
techniques that allow us to discriminate a signal of astrophysical origin from
instrumental artefacts in the interferometer data and to extract the full range
of information are some of the primary goals of the current work. Here we
report the details of a Bayesian approach to the problem of inference for
gravitational wave observations using a network of instruments, for the
computation of the Bayes factor between two hypotheses and the evaluation of
the marginalised posterior density functions of the unknown model parameters.
The numerical algorithm to tackle the notoriously difficult problem of the
evaluation of large multi-dimensional integrals is based on a technique known
as Nested Sampling, which provides an attractive alternative to more
traditional Markov-chain Monte Carlo (MCMC) methods. We discuss the details of
the implementation of this algorithm and its performance against a Gaussian
model of the background noise, considering the specific case of the signal
produced by the in-spiral of binary systems of black holes and/or neutron
stars, although the method is completely general and can be applied to other
classes of sources. We also demonstrate the utility of this approach by
introducing a new coherence test to distinguish between the presence of a
coherent signal of astrophysical origin in the data of multiple instruments and
the presence of incoherent accidental artefacts, and the effects on the
estimation of the source parameters as a function of the number of instruments
in the network.Comment: 22 page
A Bayesian parameter estimation approach to pulsar time-of-arrival analysis
The increasing sensitivities of pulsar timing arrays to ultra-low frequency
(nHz) gravitational waves promises to achieve direct gravitational wave
detection within the next 5-10 years. While there are many parallel efforts
being made in the improvement of telescope sensitivity, the detection of stable
millisecond pulsars and the improvement of the timing software, there are
reasons to believe that the methods used to accurately determine the
time-of-arrival (TOA) of pulses from radio pulsars can be improved upon. More
specifically, the determination of the uncertainties on these TOAs, which
strongly affect the ability to detect GWs through pulsar timing, may be
unreliable. We propose two Bayesian methods for the generation of pulsar TOAs
starting from pulsar "search-mode" data and pre-folded data. These methods are
applied to simulated toy-model examples and in this initial work we focus on
the issue of uncertainties in the folding period. The final results of our
analysis are expressed in the form of posterior probability distributions on
the signal parameters (including the TOA) from a single observation.Comment: 16 pages, 4 figure
Early Advanced LIGO binary neutron-star sky localization and parameter estimation
2015 will see the first observations of Advanced LIGO and the start of the
gravitational-wave (GW) advanced-detector era. One of the most promising
sources for ground-based GW detectors are binary neutron-star (BNS)
coalescences. In order to use any detections for astrophysics, we must
understand the capabilities of our parameter-estimation analysis. By simulating
the GWs from an astrophysically motivated population of BNSs, we examine the
accuracy of parameter inferences in the early advanced-detector era. We find
that sky location, which is important for electromagnetic follow-up, can be
determined rapidly (~5 s), but that sky areas may be hundreds of square
degrees. The degeneracy between component mass and spin means there is
significant uncertainty for measurements of the individual masses and spins;
however, the chirp mass is well measured (typically better than 0.1%).Comment: 4 pages, 2 figures. Published in the proceedings of Amaldi 1
The effects of LIGO detector noise on a 15-dimensional Markov-chain Monte-Carlo analysis of gravitational-wave signals
Gravitational-wave signals from inspirals of binary compact objects (black
holes and neutron stars) are primary targets of the ongoing searches by
ground-based gravitational-wave (GW) interferometers (LIGO, Virgo, and
GEO-600). We present parameter-estimation results from our Markov-chain
Monte-Carlo code SPINspiral on signals from binaries with precessing spins. Two
data sets are created by injecting simulated GW signals into either synthetic
Gaussian noise or into LIGO detector data. We compute the 15-dimensional
probability-density functions (PDFs) for both data sets, as well as for a data
set containing LIGO data with a known, loud artefact ("glitch"). We show that
the analysis of the signal in detector noise yields accuracies similar to those
obtained using simulated Gaussian noise. We also find that while the Markov
chains from the glitch do not converge, the PDFs would look consistent with a
GW signal present in the data. While our parameter-estimation results are
encouraging, further investigations into how to differentiate an actual GW
signal from noise are necessary.Comment: 11 pages, 2 figures, NRDA09 proceeding
Towards a generic test of the strong field dynamics of general relativity using compact binary coalescence: Further investigations
In this paper we elaborate on earlier work by the same authors in which a
novel Bayesian inference framework for testing the strong-field dynamics of
General Relativity using coalescing compact binaries was proposed. Unlike
methods that were used previously, our technique addresses the question whether
one or more 'testing coefficients' (e.g. in the phase) parameterizing
deviations from GR are non-zero, rather than all of them differing from zero at
the same time. The framework is well-adapted to a scenario where most sources
have low signal-to-noise ratio, and information from multiple sources as seen
in multiple detectors can readily be combined. In our previous work, we
conjectured that this framework can detect generic deviations from GR that can
in principle not be accomodated by our model waveforms, on condition that the
change in phase near frequencies where the detectors are the most sensitive is
comparable to that induced by simple shifts in the lower-order phase
coefficients of more than a few percent ( radians at 150 Hz). To
further support this claim, we perform additional numerical experiments in
Gaussian and stationary noise according to the expected Advanced LIGO/Virgo
noise curves, and coherently injecting signals into the network whose phasing
differs structurally from the predictions of GR, but with the magnitude of the
deviation still being small. We find that even then, a violation of GR can be
established with good confidence.Comment: 15 pages, 7 figures, Amaldi 9 proceeding
Inference on inspiral signals using LISA MLDC data
In this paper we describe a Bayesian inference framework for analysis of data
obtained by LISA. We set up a model for binary inspiral signals as defined for
the Mock LISA Data Challenge 1.2 (MLDC), and implemented a Markov chain Monte
Carlo (MCMC) algorithm to facilitate exploration and integration of the
posterior distribution over the 9-dimensional parameter space. Here we present
intermediate results showing how, using this method, information about the 9
parameters can be extracted from the data.Comment: Accepted for publication in Classical and Quantum Gravity, GWDAW-11
special issu
The Mock LISA Data Challenges: from Challenge 3 to Challenge 4
The Mock LISA Data Challenges are a program to demonstrate LISA data-analysis
capabilities and to encourage their development. Each round of challenges
consists of one or more datasets containing simulated instrument noise and
gravitational waves from sources of undisclosed parameters. Participants
analyze the datasets and report best-fit solutions for the source parameters.
Here we present the results of the third challenge, issued in Apr 2008, which
demonstrated the positive recovery of signals from chirping Galactic binaries,
from spinning supermassive--black-hole binaries (with optimal SNRs between ~ 10
and 2000), from simultaneous extreme-mass-ratio inspirals (SNRs of 10-50), from
cosmic-string-cusp bursts (SNRs of 10-100), and from a relatively loud
isotropic background with Omega_gw(f) ~ 10^-11, slightly below the LISA
instrument noise.Comment: 12 pages, 2 figures, proceedings of the 8th Edoardo Amaldi Conference
on Gravitational Waves, New York, June 21-26, 200
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
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