935 research outputs found
Time-frequency analysis of extreme-mass-ratio inspiral signals in mock LISA data
Extreme-mass-ratio inspirals (EMRIs) of ~ 1-10 solar-mass compact objects
into ~ million solar-mass massive black holes can serve as excellent probes of
strong-field general relativity. The Laser Interferometer Space Antenna (LISA)
is expected to detect gravitational wave signals from apprxomiately one hundred
EMRIs per year, but the data analysis of EMRI signals poses a unique set of
challenges due to their long duration and the extensive parameter space of
possible signals. One possible approach is to carry out a search for EMRI
tracks in the time-frequency domain. We have applied a time-frequency search to
the data from the Mock LISA Data Challenge (MLDC) with promising results. Our
analysis used the Hierarchical Algorithm for Clusters and Ridges to identify
tracks in the time-frequency spectrogram corresponding to EMRI sources. We then
estimated the EMRI source parameters from these tracks. In these proceedings,
we discuss the results of this analysis of the MLDC round 1.3 data.Comment: Amaldi-7 conference proceedings; requires jpconf style file
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
Electromagnetic Fields of Separable Space-Times
Carter derived the forms of the metric and the vector potentials of the
space-times in which the relativistic Schrodinger equation for the motion of a
charged particle separates. Here we show that on each `spheroidal' surface a
rotation rate exists such that relative to those rotating axes the electric and
magnetic fields are parallel and orthogonal to the spheroid which is thus an
equipotential in those axes. All the finite Carter separable systems without
magnetic monopoles or gravomagnetic NUT monopoles have the same gyromagnetic
ratio as the Dirac electron.Comment: 9 pages; accepted for publication in Class. Quantum Gra
Cosmic Swarms: A search for Supermassive Black Holes in the LISA data stream with a Hybrid Evolutionary Algorithm
We describe a hybrid evolutionary algorithm that can simultaneously search
for multiple supermassive black hole binary (SMBHB) inspirals in LISA data. The
algorithm mixes evolutionary computation, Metropolis-Hastings methods and
Nested Sampling. The inspiral of SMBHBs presents an interesting problem for
gravitational wave data analysis since, due to the LISA response function, the
sources have a bi-modal sky solution. We show here that it is possible not only
to detect multiple SMBHBs in the data stream, but also to investigate
simultaneously all the various modes of the global solution. In all cases, the
algorithm returns parameter determinations within (as estimated from
the Fisher Matrix) of the true answer, for both the actual and antipodal sky
solutions.Comment: submitted to Classical & Quantum Gravity. 19 pages, 4 figure
Classifying LISA gravitational wave burst signals using Bayesian evidence
We consider the problem of characterisation of burst sources detected with
the Laser Interferometer Space Antenna (LISA) using the multi-modal nested
sampling algorithm, MultiNest. We use MultiNest as a tool to search for
modelled bursts from cosmic string cusps, and compute the Bayesian evidence
associated with the cosmic string model. As an alternative burst model, we
consider sine-Gaussian burst signals, and show how the evidence ratio can be
used to choose between these two alternatives. We present results from an
application of MultiNest to the last round of the Mock LISA Data Challenge, in
which we were able to successfully detect and characterise all three of the
cosmic string burst sources present in the release data set. We also present
results of independent trials and show that MultiNest can detect cosmic string
signals with signal-to-noise ratio (SNR) as low as ~7 and sine-Gaussian signals
with SNR as low as ~8. In both cases, we show that the threshold at which the
sources become detectable coincides with the SNR at which the evidence ratio
begins to favour the correct model over the alternative.Comment: 21 pages, 11 figures, accepted by CQG; v2 has minor changes for
consistency with accepted versio
Facing the LISA Data Analysis Challenge
By being the first observatory to survey the source rich low frequency region
of the gravitational wave spectrum, the Laser Interferometer Space Antenna
(LISA) will revolutionize our understanding of the Cosmos. For the first time
we will be able to detect the gravitational radiation from millions of galactic
binaries, the coalescence of two massive black holes, and the inspirals of
compact objects into massive black holes. The signals from multiple sources in
each class, and possibly others as well, will be simultaneously present in the
data. To achieve the enormous scientific return possible with LISA,
sophisticated data analysis techniques must be developed which can mine the
complex data in an effort to isolate and characterize individual signals. This
proceedings paper very briefly summarizes the challenges associated with
analyzing the LISA data, the current state of affairs, and the necessary next
steps to move forward in addressing the imminent challenges.Comment: 4 pages, no figures, Proceedings paper for the TeV Particle
Astrophysics II conference held Aug 28-31 at the Univ. of Wisconsi
Novel Method for Incorporating Model Uncertainties into Gravitational Wave Parameter Estimates
Posterior distributions on parameters computed from experimental data using
Bayesian techniques are only as accurate as the models used to construct them.
In many applications these models are incomplete, which both reduces the
prospects of detection and leads to a systematic error in the parameter
estimates. In the analysis of data from gravitational wave detectors, for
example, accurate waveform templates can be computed using numerical methods,
but the prohibitive cost of these simulations means this can only be done for a
small handful of parameters. In this work a novel method to fold model
uncertainties into data analysis is proposed; the waveform uncertainty is
analytically marginalised over using with a prior distribution constructed by
using Gaussian process regression to interpolate the waveform difference from a
small training set of accurate templates. The method is well motivated, easy to
implement, and no more computationally expensive than standard techniques. The
new method is shown to perform extremely well when applied to a toy problem.
While we use the application to gravitational wave data analysis to motivate
and illustrate the technique, it can be applied in any context where model
uncertainties exist.Comment: 6 pages, 3 figures, accepted for publication in Physical Review
Letter
Constraining properties of the black hole population using LISA
LISA should detect gravitational waves from tens to hundreds of systems
containing black holes with mass in the range from 10 thousand to 10 million
solar masses. Black holes in this mass range are not well constrained by
current electromagnetic observations, so LISA could significantly enhance our
understanding of the astrophysics of such systems. In this paper, we describe a
framework for combining LISA observations to make statements about massive
black hole populations. We summarise the constraints that LISA observations of
extreme-mass-ratio inspirals might be able to place on the mass function of
black holes in the LISA range. We also describe how LISA observations can be
used to choose between different models for the hierarchical growth of
structure in the early Universe. We consider four models that differ in their
prescription for the initial mass distribution of black hole seeds, and in the
efficiency of accretion onto the black holes. We show that with as little as 3
months of LISA data we can clearly distinguish between these models, even under
relatively pessimistic assumptions about the performance of the detector and
our knowledge of the gravitational waveforms.Comment: 12 pages, 3 figures, submitted to Class. Quantum Grav. for
proceedings of 8th LISA Symposium; v2 minor changes for consistency with
accepted versio
Gravitational-Wave Signature of an Inspiral into a Supermassive Horizonless Object
Event horizons are among the most intriguing of general relativity's
predictions. Although on firm theoretical footing, direct indications of their
existence have yet to be observed. With this motivation in mind, we explore
here the possibility of finding a signature for event horizons in the
gravitational waves (GWs) produced during the inspiral of stellar-mass compact
objects (COs) into the supermassive () objects that lie at
the center of most galaxies. Such inspirals will be a major source for LISA,
the future space-based GW observatory. We contrast supermassive black holes
with models in which the central object is a supermassive boson star (SMBS).
Provided the COs interact only gravitationally with the SMBS, stable orbits
exist not just outside the Schwarzschild radius but also inside the surface of
the SMBS as well. The absence of an event horizon allows GWs from these orbits
to be observed. Here we solve for the metric in the interior of a fairly
generic class of SMBS and evolve the trajectory of an inspiraling CO from the
Schwarzschild exterior through the plunge into the exotic SMBS interior. We
calculate the approximate waveforms for GWs emitted during this inspiral.
Geodesics within the SMBS surface will exhibit extreme pericenter precession
and other features making the emitted GWs readily distinguishable from those
emitted during an inspiral into a black hole.Comment: 20 pages, 9 figures, submitted to PR
Reconstructing the massive black hole cosmic history through gravitational waves
The massive black holes we observe in galaxies today are the natural
end-product of a complex evolutionary path, in which black holes seeded in
proto-galaxies at high redshift grow through cosmic history via a sequence of
mergers and accretion episodes. Electromagnetic observations probe a small
subset of the population of massive black holes (namely, those that are active
or those that are very close to us), but planned space-based gravitational-wave
observatories such as the Laser Interferometer Space Antenna (LISA) can measure
the parameters of ``electromagnetically invisible'' massive black holes out to
high redshift. In this paper we introduce a Bayesian framework to analyze the
information that can be gathered from a set of such measurements. Our goal is
to connect a set of massive black hole binary merger observations to the
underlying model of massive black hole formation. In other words, given a set
of observed massive black hole coalescences, we assess what information can be
extracted about the underlying massive black hole population model. For
concreteness we consider ten specific models of massive black hole formation,
chosen to probe four important (and largely unconstrained) aspects of the input
physics used in structure formation simulations: seed formation, metallicity
``feedback'', accretion efficiency and accretion geometry. For the first time
we allow for the possibility of ``model mixing'', by drawing the observed
population from some combination of the ``pure'' models that have been
simulated. A Bayesian analysis allows us to recover a posterior probability
distribution for the ``mixing parameters'' that characterize the fractions of
each model represented in the observed distribution. Our work shows that LISA
has enormous potential to probe the underlying physics of structure formation.Comment: 24 pages, 16 figures, submitted to Phys. Rev.
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