191 research outputs found
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
The influence of short term variations in AM CVn systems on LISA measurements
We study the effect of short term variations of the evolution of AM CVn
systems on their gravitational wave emissions and in particular LISA
observations. We model the systems according to their equilibrium mass-transfer
evolution as driven by gravitational wave emission and tidal interaction, and
determine their reaction to a sudden perturbation of the system. This is
inspired by the suggestion to explain the orbital period evolution of the
ultra-compact binary systems V407 Vul and RX-J0806+1527 by non-equilibrium mass
transfer. The characteristics of the emitted gravitational wave signal are
deduced from a Taylor expansion of a Newtonian quadrupolar emission model, and
the changes in signal structure as visible to the LISA mission are determined.
We show that short term variations can significantly change the higher order
terms in the expansion, and thus lead to spurious (non) detection of frequency
derivatives. This may hamper the estimation of the parameters of the system, in
particular their masses and distances. However, we find that overall detection
is still secured as signals still can be described by general templates. We
conclude that a better modelling of the effects of short term variations is
needed to prepare the community for astrophysical evaluations of real
gravitational wave data of AM CVn systems.Comment: 5 pages, 3 figures, accepted for publication in MNRAS Letter
LISA astronomy of double white dwarf binary systems
The Laser Interferometer Space Antenna (LISA) will provide the largest
observational sample of (interacting) double white dwarf binaries, whose
evolution is driven by radiation reaction and other effects, such as tides and
mass transfer. We show that, depending on the actual physical parameters of a
source, LISA will be able to provide very different quality of information: for
some systems LISA can test unambiguously the physical processes driving the
binary evolution, for others it can simply detect a binary without allowing us
to untangle the source parameters and therefore shed light on the physics at
work. We also highlight that simultaneous surveys with GAIA and/or optical
telescopes that are and will become available can radically improve the quality
of the information that can be obtained.Comment: accepted for publication in ApJLetter
A Markov Chain Monte Carlo approach to the study of massive black hole binary systems with LISA
The Laser Interferometer Space Antenna (LISA) will produce a data stream
containing a vast number of overlapping sources: from strong signals generated
by the coalescence of massive black hole binary systems to much weaker
radiation form sub-stellar mass compact binaries and extreme-mass ratio
inspirals. It has been argued that the observation of weak signals could be
hampered by the presence of loud ones and that they first need to be removed to
allow such observations. Here we consider a different approach in which sources
are studied simultaneously within the framework of Bayesian inference. We
investigate the simplified case in which the LISA data stream contains
radiation from a massive black hole binary system superimposed over a (weaker)
quasi-monochromatic waveform generated by a white dwarf binary. We derive the
posterior probability density function of the model parameters using an
automatic Reversible Jump Markov Chain Monte Carlo algorithm (RJMCMC). We show
that the information about the sources and noise are retrieved at the expected
level of accuracy without the need of removing the stronger signal. Our
analysis suggests that this approach is worth pursuing further and should be
considered for the actual analysis of the LISA data.Comment: submitted to cqg as GWDAW-10 conference proceedings, 10 pages, 4
figures, some changes to plots and numerical detail
Tackling gravity wave confusion noise with template optimizers
The Mock LISA Data Challenge 4.0 simulated the joint two-year recording of gravitational wave signals from mergers of spinning black holes, extreme mass ratio inspirals, Galactic white dwarf binaries, bursts from cosmic strings, and a stochastic backgroundâall over LISA instrument noise. We analysed this data using a global multi-start box and bound optimization scheme, incorporating multi-dimensional Nelder Mead simplex 2 optimization. Our scheme identified 2658 binaries. Of these, 2246 were found to systematically decompose the power in a strong spinning black hole merger into a white dwarf binary transform . The remaining 416 binaries were identified with a false alarm rate of ~ 23%
Ninja data analysis with a detection pipeline based on the Hilbert-Huang Transform
The Ninja data analysis challenge allowed the study of the sensitivity of
data analysis pipelines to binary black hole numerical relativity waveforms in
simulated Gaussian noise at the design level of the LIGO observatory and the
VIRGO observatory. We analyzed NINJA data with a pipeline based on the Hilbert
Huang Transform, utilizing a detection stage and a characterization stage:
detection is performed by triggering on excess instantaneous power,
characterization is performed by displaying the kernel density enhanced (KD)
time-frequency trace of the signal. Using the simulated data based on the two
LIGO detectors, we were able to detect 77 signals out of 126 above SNR 5 in
coincidence, with 43 missed events characterized by signal to noise ratio SNR
less than 10. Characterization of the detected signals revealed the merger part
of the waveform in high time and frequency resolution, free from time-frequency
uncertainty. We estimated the timelag of the signals between the detectors
based on the optimal overlap of the individual KD time-frequency maps, yielding
estimates accurate within a fraction of a millisecond for half of the events. A
coherent addition of the data sets according to the estimated timelag
eventually was used in a characterization of the event.Comment: Accepted for publication in CQG, special issue NRDA proceedings 200
Gravitational-Wave Astronomy with Inspiral Signals of Spinning Compact-Object Binaries
Inspiral signals from binary compact objects (black holes and neutron stars)
are primary targets of the ongoing searches by ground-based gravitational-wave
interferometers (LIGO, Virgo, GEO-600 and TAMA-300). We present
parameter-estimation simulations for inspirals of black-hole--neutron-star
binaries using Markov-chain Monte-Carlo methods. For the first time, we have
both estimated the parameters of a binary inspiral source with a spinning
component and determined the accuracy of the parameter estimation, for
simulated observations with ground-based gravitational-wave detectors. We
demonstrate that we can obtain the distance, sky position, and binary
orientation at a higher accuracy than previously suggested in the literature.
For an observation of an inspiral with sufficient spin and two or three
detectors we find an accuracy in the determination of the sky position of
typically a few tens of square degrees.Comment: v2: major conceptual changes, 4 pages, 1 figure, 1 table, submitted
to ApJ
Detecting extreme mass ratio inspiral events in LISA data using the Hierarchical Algorithm for Clusters and Ridges (HACR)
One of the most exciting prospects for the Laser Interferometer Space Antenna
(LISA) is the detection of gravitational waves from the inspirals of
stellar-mass compact objects into supermassive black holes. Detection of these
sources is an extremely challenging computational problem due to the large
parameter space and low amplitude of the signals. However, recent work has
suggested that the nearest extreme mass ratio inspiral (EMRI) events will be
sufficiently loud that they might be detected using computationally cheap,
template-free techniques, such as a time-frequency analysis. In this paper, we
examine a particular time-frequency algorithm, the Hierarchical Algorithm for
Clusters and Ridges (HACR). This algorithm searches for clusters in a power map
and uses the properties of those clusters to identify signals in the data. We
find that HACR applied to the raw spectrogram performs poorly, but when the
data is binned during the construction of the spectrogram, the algorithm can
detect typical EMRI events at distances of up to Gpc. This is a little
further than the simple Excess Power method that has been considered
previously. We discuss the HACR algorithm, including tuning for single and
multiple sources, and illustrate its performance for detection of typical EMRI
events, and other likely LISA sources, such as white dwarf binaries and
supermassive black hole mergers. We also discuss how HACR cluster properties
could be used for parameter extraction.Comment: 21 pages, 11 figures, submitted to Class. Quantum Gravity. Modified
and shortened in light of referee's comments. Updated results consider tuning
over all three HACR thresholds, and show 10-15% improvement in detection rat
Methods for detection and characterization of signals in noisy data with the Hilbert-Huang Transform
The Hilbert-Huang Transform is a novel, adaptive approach to time series
analysis that does not make assumptions about the data form. Its adaptive,
local character allows the decomposition of non-stationary signals with
hightime-frequency resolution but also renders it susceptible to degradation
from noise. We show that complementing the HHT with techniques such as
zero-phase filtering, kernel density estimation and Fourier analysis allows it
to be used effectively to detect and characterize signals with low signal to
noise ratio.Comment: submitted to PRD, 10 pages, 9 figures in colo
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