186 research outputs found
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
Localizing gravitational wave sources with optical telescopes and combining electromagnetic and gravitational wave data
Neutron star binaries, which are among the most promising sources for the
direct detection of gravitational waves (GW) by ground based detectors, are
also potential electromagnetic (EM) emitters. Gravitational waves will provide
a new window to observe these events and hopefully give us glimpses of new
astrophysics. In this paper, we discuss how EM information of these events can
considerably improve GW parameter estimation both in terms of accuracy and
computational power requirement. And then in return how GW sky localization can
help EM astronomers in follow-up studies of sources which did not yield any
prompt emission. We discuss how both EM source information and GW source
localization can be used in a framework of multi-messenger astronomy. We
illustrate how the large error regions in GW sky localizations can be handled
in conducting optical astronomy in the advance detector era. We show some
preliminary results in the context of an array of optical telescopes called
BlackGEM, dedicated for optical follow-up of GW triggers, that is being
constructed in La Silla, Chile and is expected to operate concurrent to the
advanced GW detectors.Comment: 8 pages, 8 figures, Proceeding for Sant Cugat Forum for Astrophysic
Bayesian parameter estimation in the second LISA Pathfinder Mock Data Challenge
A main scientific output of the LISA Pathfinder mission is to provide a noise
model that can be extended to the future gravitational wave observatory, LISA.
The success of the mission depends thus upon a deep understanding of the
instrument, especially the ability to correctly determine the parameters of the
underlying noise model. In this work we estimate the parameters of a simplified
model of the LISA Technology Package (LTP) instrument. We describe the LTP by
means of a closed-loop model that is used to generate the data, both injected
signals and noise. Then, parameters are estimated using a Bayesian framework
and it is shown that this method reaches the optimal attainable error, the
Cramer-Rao bound. We also address an important issue for the mission: how to
efficiently combine the results of different experiments to obtain a unique set
of parameters describing the instrument.Comment: 14 pages, 4 figures, submitted to PR
Parameter estimation of spinning binary inspirals using Markov-chain Monte Carlo
We present a Markov-chain Monte-Carlo (MCMC) technique to study the source
parameters of gravitational-wave signals from the inspirals of stellar-mass
compact binaries detected with ground-based gravitational-wave detectors such
as LIGO and Virgo, for the case where spin is present in the more massive
compact object in the binary. We discuss aspects of the MCMC algorithm that
allow us to sample the parameter space in an efficient way. We show sample runs
that illustrate the possibilities of our MCMC code and the difficulties that we
encounter.Comment: 10 pages, 2 figures, submitted to Classical and Quantum Gravit
Coherent Bayesian analysis of inspiral signals
We present in this paper a Bayesian parameter estimation method for the
analysis of interferometric gravitational wave observations of an inspiral of
binary compact objects using data recorded simultaneously by a network of
several interferometers at different sites. We consider neutron star or black
hole inspirals that are modeled to 3.5 post-Newtonian (PN) order in phase and
2.5 PN in amplitude. Inference is facilitated using Markov chain Monte Carlo
methods that are adapted in order to efficiently explore the particular
parameter space. Examples are shown to illustrate how and what information
about the different parameters can be derived from the data. This study uses
simulated signals and data with noise characteristics that are assumed to be
defined by the LIGO and Virgo detectors operating at their design
sensitivities. Nine parameters are estimated, including those associated with
the binary system, plus its location on the sky. We explain how this technique
will be part of a detection pipeline for binary systems of compact objects with
masses up to 20 \sunmass, including cases where the ratio of the individual
masses can be extreme.Comment: Accepted for publication in Classical and Quantum Gravity, Special
issue for GWDAW-1
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
A Student-t based filter for robust signal detection
The search for gravitational-wave signals in detector data is often hampered
by the fact that many data analysis methods are based on the theory of
stationary Gaussian noise, while actual measurement data frequently exhibit
clear departures from these assumptions. Deriving methods from models more
closely reflecting the data's properties promises to yield more sensitive
procedures. The commonly used matched filter is such a detection method that
may be derived via a Gaussian model. In this paper we propose a generalized
matched-filtering technique based on a Student-t distribution that is able to
account for heavier-tailed noise and is robust against outliers in the data. On
the technical side, it generalizes the matched filter's least-squares method to
an iterative, or adaptive, variation. In a simplified Monte Carlo study we show
that when applied to simulated signals buried in actual interferometer noise it
leads to a higher detection rate than the usual ("Gaussian") matched filter.Comment: 17 pages, 6 figure
Bayesian inference on compact binary inspiral gravitational radiation signals in interferometric data
Presented is a description of a Markov chain Monte Carlo (MCMC) parameter
estimation routine for use with interferometric gravitational radiational data
in searches for binary neutron star inspiral signals. Five parameters
associated with the inspiral can be estimated, and summary statistics are
produced. Advanced MCMC methods were implemented, including importance
resampling and prior distributions based on detection probability, in order to
increase the efficiency of the code. An example is presented from an
application using realistic, albeit fictitious, data.Comment: submitted to Classical and Quantum Gravity. 14 pages, 5 figure
Binary black hole spectroscopy
We study parameter estimation with post-Newtonian (PN) gravitational
waveforms for the quasi-circular, adiabatic inspiral of spinning binary compact
objects. The performance of amplitude-corrected waveforms is compared with that
of the more commonly used restricted waveforms, in Advanced LIGO and EGO. With
restricted waveforms, the properties of the source can only be extracted from
the phasing. For amplitude-corrected waveforms, the spectrum encodes a wealth
of additional information, which leads to dramatic improvements in parameter
estimation. At distances of Mpc, the full PN waveforms allow for
high-accuracy parameter extraction for total mass up to several hundred solar
masses, while with the restricted ones the errors are steep functions of mass,
and accurate parameter estimation is only possible for relatively light stellar
mass binaries. At the low-mass end, the inclusion of amplitude corrections
reduces the error on the time of coalescence by an order of magnitude in
Advanced LIGO and a factor of 5 in EGO compared to the restricted waveforms; at
higher masses these differences are much larger. The individual component
masses, which are very poorly determined with restricted waveforms, become
measurable with high accuracy if amplitude-corrected waveforms are used, with
errors as low as a few percent in Advanced LIGO and a few tenths of a percent
in EGO. The usual spin-orbit parameter is also poorly determined with
restricted waveforms (except for low-mass systems in EGO), but the full
waveforms give errors that are small compared to the largest possible value
consistent with the Kerr bound. This suggests a way of finding out if one or
both of the component objects violate this bound. We also briefly discuss the
effect of amplitude corrections on parameter estimation in Initial LIGO.Comment: 28 pages, many figures. Final version accepted by CQG. More in-depth
treatment of component mass errors and detectability of Kerr bound
violations; improved presentatio
Coherent Bayesian inference on compact binary inspirals using a network of interferometric gravitational wave detectors
Presented in this paper is a Markov chain Monte Carlo (MCMC) routine for
conducting coherent parameter estimation for interferometric gravitational wave
observations of an inspiral of binary compact objects using data from multiple
detectors. The MCMC technique uses data from several interferometers and infers
all nine of the parameters (ignoring spin) associated with the binary system,
including the distance to the source, the masses, and the location on the sky.
The Metropolis-algorithm utilises advanced MCMC techniques, such as importance
resampling and parallel tempering. The data is compared with time-domain
inspiral templates that are 2.5 post-Newtonian (PN) in phase and 2.0 PN in
amplitude. Our routine could be implemented as part of an inspiral detection
pipeline for a world wide network of detectors. Examples are given for
simulated signals and data as seen by the LIGO and Virgo detectors operating at
their design sensitivity.Comment: 10 pages, 4 figure
- …