3,125 research outputs found
LISA Data Analysis using MCMC methods
The Laser Interferometer Space Antenna (LISA) is expected to simultaneously
detect many thousands of low frequency gravitational wave signals. This
presents a data analysis challenge that is very different to the one
encountered in ground based gravitational wave astronomy. LISA data analysis
requires the identification of individual signals from a data stream containing
an unknown number of overlapping signals. Because of the signal overlaps, a
global fit to all the signals has to be performed in order to avoid biasing the
solution. However, performing such a global fit requires the exploration of an
enormous parameter space with a dimension upwards of 50,000. Markov Chain Monte
Carlo (MCMC) methods offer a very promising solution to the LISA data analysis
problem. MCMC algorithms are able to efficiently explore large parameter
spaces, simultaneously providing parameter estimates, error analyses and even
model selection. Here we present the first application of MCMC methods to
simulated LISA data and demonstrate the great potential of the MCMC approach.
Our implementation uses a generalized F-statistic to evaluate the likelihoods,
and simulated annealing to speed convergence of the Markov chains. As a final
step we super-cool the chains to extract maximum likelihood estimates, and
estimates of the Bayes factors for competing models. We find that the MCMC
approach is able to correctly identify the number of signals present, extract
the source parameters, and return error estimates consistent with Fisher
information matrix predictions.Comment: 14 pages, 7 figure
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
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
Markov chain Monte Carlo searches for Galactic binaries in Mock LISA Data Challenge 1B data sets
We are developing a Bayesian approach based on Markov chain Monte Carlo
techniques to search for and extract information about white dwarf binary
systems with the Laser Interferometer Space Antenna (LISA). Here we present
results obtained by applying an initial implementation of this method to some
of the data sets released in Round 1B of the Mock LISA Data Challenges. For
Challenges 1B.1.1a and 1b the signals were recovered with parameters lying
within the 95.5% posterior probability interval and the correlation between the
true and recovered waveform is in excess of 99%. Results were not submitted for
Challenge 1B.1.1c due to some convergence problems of the algorithms, despite
this, the signal was detected in a search over a 2 mHz band.Comment: 11 pages, 5 figures, 12th GWDAW (Gravitational Wave Data Analysis
Workshop). Accepted for publication in CQ
A Bayesian approach to the study of white dwarf binaries in LISA data: The application of a reversible jump Markov chain Monte Carlo method
The Laser Interferometer Space Antenna (LISA) defines new demands on data
analysis efforts in its all-sky gravitational wave survey, recording
simultaneously thousands of galactic compact object binary foreground sources
and tens to hundreds of background sources like binary black hole mergers and
extreme mass ratio inspirals. We approach this problem with an adaptive and
fully automatic Reversible Jump Markov Chain Monte Carlo sampler, able to
sample from the joint posterior density function (as established by Bayes
theorem) for a given mixture of signals "out of the box'', handling the total
number of signals as an additional unknown parameter beside the unknown
parameters of each individual source and the noise floor. We show in examples
from the LISA Mock Data Challenge implementing the full response of LISA in its
TDI description that this sampler is able to extract monochromatic Double White
Dwarf signals out of colored instrumental noise and additional foreground and
background noise successfully in a global fitting approach. We introduce 2
examples with fixed number of signals (MCMC sampling), and 1 example with
unknown number of signals (RJ-MCMC), the latter further promoting the idea
behind an experimental adaptation of the model indicator proposal densities in
the main sampling stage. We note that the experienced runtimes and degeneracies
in parameter extraction limit the shown examples to the extraction of a low but
realistic number of signals.Comment: 18 pages, 9 figures, 3 tables, accepted for publication in PRD,
revised versio
A Solution to the Galactic Foreground Problem for LISA
Low frequency gravitational wave detectors, such as the Laser Interferometer
Space Antenna (LISA), will have to contend with large foregrounds produced by
millions of compact galactic binaries in our galaxy. While these galactic
signals are interesting in their own right, the unresolved component can
obscure other sources. The science yield for the LISA mission can be improved
if the brighter and more isolated foreground sources can be identified and
regressed from the data. Since the signals overlap with one another we are
faced with a ``cocktail party'' problem of picking out individual conversations
in a crowded room. Here we present and implement an end-to-end solution to the
galactic foreground problem that is able to resolve tens of thousands of
sources from across the LISA band. Our algorithm employs a variant of the
Markov Chain Monte Carlo (MCMC) method, which we call the Blocked Annealed
Metropolis-Hastings (BAM) algorithm. Following a description of the algorithm
and its implementation, we give several examples ranging from searches for a
single source to searches for hundreds of overlapping sources. Our examples
include data sets from the first round of Mock LISA Data Challenges.Comment: 19 pages, 27 figure
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