399 research outputs found
Random template banks and relaxed lattice coverings
Template-based searches for gravitational waves are often limited by the
computational cost associated with searching large parameter spaces. The study
of efficient template banks, in the sense of using the smallest number of
templates, is therefore of great practical interest. The "traditional" approach
to template-bank construction requires every point in parameter space to be
covered by at least one template, which rapidly becomes inefficient at higher
dimensions. Here we study an alternative approach, where any point in parameter
space is covered only with a given probability < 1. We find that by giving up
complete coverage in this way, large reductions in the number of templates are
possible, especially at higher dimensions. The prime examples studied here are
"random template banks", in which templates are placed randomly with uniform
probability over the parameter space. In addition to its obvious simplicity,
this method turns out to be surprisingly efficient. We analyze the statistical
properties of such random template banks, and compare their efficiency to
traditional lattice coverings. We further study "relaxed" lattice coverings
(using Zn and An* lattices), which similarly cover any signal location only
with probability < 1. The relaxed An* lattice is found to yield the most
efficient template banks at low dimensions (n < 10), while random template
banks increasingly outperform any other method at higher dimensions.Comment: 13 pages, 10 figures, submitted to PR
Targeted search for continuous gravitational waves: Bayesian versus maximum-likelihood statistics
We investigate the Bayesian framework for detection of continuous
gravitational waves (GWs) in the context of targeted searches, where the phase
evolution of the GW signal is assumed to be known, while the four amplitude
parameters are unknown. We show that the orthodox maximum-likelihood statistic
(known as F-statistic) can be rediscovered as a Bayes factor with an unphysical
prior in amplitude parameter space. We introduce an alternative detection
statistic ("B-statistic") using the Bayes factor with a more natural amplitude
prior, namely an isotropic probability distribution for the orientation of GW
sources. Monte-Carlo simulations of targeted searches show that the resulting
Bayesian B-statistic is more powerful in the Neyman-Pearson sense (i.e. has a
higher expected detection probability at equal false-alarm probability) than
the frequentist F-statistic.Comment: 12 pages, presented at GWDAW13, to appear in CQ
Building a stochastic template bank for detecting massive black hole binaries
Coalescence of two massive black holes is the strongest and most promising
source for LISA. In fact, gravitational signal from the end of inspiral and
merger will be detectable throughout the Universe. In this article we describe
the first step in the two-step hierarchical search for gravitational wave
signal from the inspiraling massive BH binaries. It is based on the routinely
used in the ground base gravitational wave astronomy method of filtering the
data through the bank of templates. However we use a novel Monte-Carlo based
(stochastic) method to lay a grid in the parameter space, and we use the
likelihood maximized analytically over some parameters, known as F-statistic,
as a detection statistic. We build a coarse template bank to detect
gravitational wave signals and to make preliminary parameter estimation. The
best candidates will be followed up using Metropolis-Hasting stochastic search
to refine the parameter estimation. We demonstrate the performance of the
method by applying it to the Mock LISA data challenge 1B (training data set).Comment: revtex4, 8 figure
Gravitational wave background from rotating neutron stars
The background of gravitational waves produced by the ensemble of rotating
neutron stars (which includes pulsars, magnetars and gravitars) is
investigated. A formula for \Omega(f) (commonly used to quantify the
background) is derived, properly taking into account the time evolution of the
systems since their formation until the present day. Moreover, the formula
allows one to distinguish the different parts of the background: the
unresolvable (which forms a stochastic background) and the resolvable. Several
estimations of the background are obtained, for different assumptions on the
parameters that characterize neutron stars and their population. In particular,
different initial spin period distributions lead to very different results. For
one of the models, with slow initial spins, the detection of the background can
be rejected. However, other models do predict the detection of the background
by the future ground-based gravitational wave detector ET. A robust upper limit
for the background of rotating neutron stars is obtained; it does not exceed
the detection threshold of two cross-correlated Advanced LIGO interferometers.
If gravitars exist and constitute more than a few percent of the neutron star
population, then they produce an unresolvable background that could be detected
by ET. Under the most reasonable assumptions on the parameters characterizing a
neutron star, the background is too faint. Previous papers have suggested
neutron star models in which large magnetic fields (like the ones that
characterize magnetars) induce big deformations in the star, which produce a
stronger emission of gravitational radiation. Considering the most optimistic
(in terms of the detection of gravitational waves) of these models, an upper
limit for the background produced by magnetars is obtained; it could be
detected by ET, but not by BBO or DECIGO.Comment: 25 pages, 15 figure
A hierarchical search for gravitational waves from supermassive black hole binary mergers
We present a method to search for gravitational waves from coalescing
supermassive binary black holes in LISA data. The search utilizes the
-statistic to maximize over, and determine the values of, the
extrinsic parameters of the binary system. The intrinsic parameters are
searched over hierarchically using stochastically generated multi-dimensional
template banks to recover the masses and sky locations of the binary. We
present the results of this method applied to the mock LISA data Challenge 1B
data set.Comment: 11 pages, 2 figures, for GWDAW-12 proceedings edition of CQ
Random template placement and prior information
In signal detection problems, one is usually faced with the task of searching
a parameter space for peaks in the likelihood function which indicate the
presence of a signal. Random searches have proven to be very efficient as well
as easy to implement, compared e.g. to searches along regular grids in
parameter space. Knowledge of the parameterised shape of the signal searched
for adds structure to the parameter space, i.e., there are usually regions
requiring to be densely searched while in other regions a coarser search is
sufficient. On the other hand, prior information identifies the regions in
which a search will actually be promising or may likely be in vain. Defining
specific figures of merit allows one to combine both template metric and prior
distribution and devise optimal sampling schemes over the parameter space. We
show an example related to the gravitational wave signal from a binary inspiral
event. Here the template metric and prior information are particularly
contradictory, since signals from low-mass systems tolerate the least mismatch
in parameter space while high-mass systems are far more likely, as they imply a
greater signal-to-noise ratio (SNR) and hence are detectable to greater
distances. The derived sampling strategy is implemented in a Markov chain Monte
Carlo (MCMC) algorithm where it improves convergence.Comment: Proceedings of the 8th Edoardo Amaldi Conference on Gravitational
Waves. 7 pages, 4 figure
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
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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