275 research outputs found
The Missing Link: Bayesian Detection and Measurement of Intermediate-Mass Black-Hole Binaries
We perform Bayesian analysis of gravitational-wave signals from non-spinning,
intermediate-mass black-hole binaries (IMBHBs) with observed total mass,
, from to and
mass ratio 1\mbox{--}4 using advanced LIGO and Virgo detectors. We employ
inspiral-merger-ringdown waveform models based on the effective-one-body
formalism and include subleading modes of radiation beyond the leading
mode. The presence of subleading modes increases signal power for inclined
binaries and allows for improved accuracy and precision in measurements of the
masses as well as breaking of extrinsic parameter degeneracies. For low total
masses, , the observed chirp
mass ( being the
symmetric mass ratio) is better measured. In contrast, as increasing power
comes from merger and ringdown, we find that the total mass
has better relative precision than . Indeed, at high
(), the signal resembles a
burst and the measurement thus extracts the dominant frequency of the signal
that depends on . Depending on the binary's inclination, at
signal-to-noise ratio (SNR) of , uncertainties in can be
as large as \sim 20 \mbox{--}25\% while uncertainties in are \sim 50 \mbox{--}60\% in binaries with unequal masses (those
numbers become versus in more symmetric binaries).
Although large, those uncertainties will establish the existence of IMBHs. Our
results show that gravitational-wave observations can offer a unique tool to
observe and understand the formation, evolution and demographics of IMBHs,
which are difficult to observe in the electromagnetic window. (abridged)Comment: 17 pages, 9 figures, 2 tables; updated to reflect published versio
High Energy Variability Of Synchrotron-Self Compton Emitting Sources: Why One Zone Models Do Not Work And How We Can Fix It
With the anticipated launch of GLAST, the existing X-ray telescopes, and the
enhanced capabilities of the new generation of TeV telescopes, developing tools
for modeling the variability of high energy sources such as blazars is becoming
a high priority. We point out the serious, innate problems one zone
synchrotron-self Compton models have in simulating high energy variability. We
then present the first steps toward a multi zone model where non-local, time
delayed Synchrotron-self Compton electron energy losses are taken into account.
By introducing only one additional parameter, the length of the system, our
code can simulate variability properly at Compton dominated stages, a situation
typical of flaring systems. As a first application, we were able to reproduce
variability similar to that observed in the case of the puzzling `orphan' TeV
flares that are not accompanied by a corresponding X-ray flare.Comment: to appear in the 1st GLAST symposium proceeding
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Bayesian methods for gravitational waves and neural networks
Einstein’s general theory of relativity has withstood 100 years of testing
and will soon be facing one of its toughest challenges. In a few years
we expect to be entering the era of the first direct observations of gravitational
waves. These are tiny perturbations of space-time that are generated
by accelerating matter and affect the measured distances between
two points. Observations of these using the laser interferometers, which
are the most sensitive length-measuring devices in the world, will allow
us to test models of interactions in the strong field regime of gravity and
eventually general relativity itself.
I apply the tools of Bayesian inference for the examination of gravitational
wave data from the LIGO and Virgo detectors. This is used for signal
detection and estimation of the source parameters. I quantify the ability
of a network of ground-based detectors to localise a source position
on the sky for electromagnetic follow-up. Bayesian criteria are also applied
to separating real signals from glitches in the detectors. These same
tools and lessons can also be applied to the type of data expected from
planned space-based detectors. Using simulations from the Mock LISA
Data Challenges, I analyse our ability to detect and characterise both burst
and continuous signals. The two seemingly different signal types will be
overlapping and confused with one another for a space-based detector; my
analysis shows that we will be able to separate and identify many signals
present.
Data sets and astrophysical models are continuously increasing in complexity.
This will create an additional computational burden for performing
Bayesian inference and other types of data analysis. I investigate the
application of the MOPED algorithm for faster parameter estimation and
data compression. I find that its shortcomings make it a less favourable
candidate for further implementation.
The framework of an artificial neural network is a simple model for the
structure of a brain which can “learn” functional relationships between sets
of inputs and outputs. I describe an algorithm developed for the training of
feed-forward networks on pre-calculated data sets. The trained networks
can then be used for fast prediction of outputs for new sets of inputs. After
demonstrating capabilities on toy data sets, I apply the ability of the
network to classifying handwritten digits from the MNIST database and
measuring ellipticities of galaxies in the Mapping Dark Matter challenge.
The power of neural networks for learning and rapid prediction is also
useful in Bayesian inference where the likelihood function is computationally
expensive. The new BAMBI algorithm is detailed, in which our
network training algorithm is combined with the nested sampling algorithm
MULTINEST to provide rapid Bayesian inference. Using samples
from the normal inference, a network is trained on the likelihood function
and eventually used in its place. This is able to provide significant increase
in the speed of Bayesian inference while returning identical results. The
trained networks can then be used for extremely rapid follow-up analyses
with different priors, obtaining orders of magnitude of speed increase.
Learning how to apply the tools of Bayesian inference for the optimal
recovery of gravitational wave signals will provide the most scientific information
when the first detections are made. Complementary to this, the
improvement of our analysis algorithms to provide the best results in less
time will make analysis of larger and more complicated models and data
sets practical
Modeling the Swift BAT Trigger Algorithm with Machine Learning
To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. (2014) is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of approximately greater than 97% (approximately less than 3% error), which is a significant improvement on a cut in GRB flux which has an accuracy of 89:6% (10:4% error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of eta(sub 0) approximately 0.48(+0.41/-0.23) Gpc(exp -3) yr(exp -1) with power-law indices of eta(sub 1) approximately 1.7(+0.6/-0.5) and eta(sub 2) approximately -5.9(+5.7/-0.1) for GRBs above and below a break point of z(sub 1) approximately 6.8(+2.8/-3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting. The code used in this is analysis is publicly available online
A multi-zone model for simulating the high energy variability of TeV blazars
We present a time-dependent multi-zone code for simulating the variability of
Synchrotron-Self Compton (SSC) sources. The code adopts a multi-zone pipe
geometry for the emission region, appropriate for simulating emission from a
standing or propagating shock in a collimated jet. Variations in the injection
of relativistic electrons in the inlet propagate along the length of the pipe
cooling radiatively. Our code for the first time takes into account the
non-local, time-retarded nature of synchrotron self-Compton (SSC) losses that
are thought to be dominant in TeV blazars. The observed synchrotron and SSC
emission is followed self-consistently taking into account light travel time
delays. At any given time, the emitting portion of the pipe depends on the
frequency and the nature of the variation followed. Our simulation employs only
one additional physical parameter relative to one-zone models, that of the pipe
length and is computationally very efficient, using simplified expressions for
the SSC processes. The code will be useful for observers modeling GLAST, TeV,
and X-ray observations of SSC blazars.Comment: ApJ, accepte
Parameter estimation on gravitational waves from neutron-star binaries with spinning components
Inspiraling binary neutron stars are expected to be one of the most
significant sources of gravitational-wave signals for the new generation of
advanced ground-based detectors. We investigate how well we could hope to
measure properties of these binaries using the Advanced LIGO detectors, which
began operation in September 2015. We study an astrophysically motivated
population of sources (binary components with masses
-- and spins of less than )
using the full LIGO analysis pipeline. While this simulated population covers
the observed range of potential binary neutron-star sources, we do not exclude
the possibility of sources with parameters outside these ranges; given the
existing uncertainty in distributions of mass and spin, it is critical that
analyses account for the full range of possible mass and spin configurations.
We find that conservative prior assumptions on neutron-star mass and spin lead
to average fractional uncertainties in component masses of , with
little constraint on spins (the median upper limit on the spin of the
more massive component is ). Stronger prior constraints on
neutron-star spins can further constrain mass estimates, but only marginally.
However, we find that the sky position and luminosity distance for these
sources are not influenced by the inclusion of spin; therefore, if LIGO detects
a low-spin population of BNS sources, less computationally expensive results
calculated neglecting spin will be sufficient for guiding electromagnetic
follow-up.Comment: 10 pages, 9 figure
Parameter Estimation for Binary Neutron-star Coalescences with Realistic Noise during the Advanced LIGO Era
Advanced ground-based gravitational-wave (GW) detectors begin operation imminently. Their intended goal is not only to make the first direct detection of GWs, but also to make inferences about the source systems. Binary neutron-star mergers are among the most promising sources. We investigate the performance of the parameter-estimation (PE) pipeline that will be used during the first observing run of the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) in 2015: we concentrate on the ability to reconstruct the source location on the sky, but also consider the ability to measure masses and the distance. Accurate, rapid sky localization is necessary to alert electromagnetic (EM) observatories so that they can perform follow-up searches for counterpart transient events. We consider PE accuracy in the presence of non-stationary, non-Gaussian noise. We find that the character of the noise makes negligible difference to the PE performance at a given signal-to-noise ratio. The source luminosity distance can only be poorly constrained, since the median 90% (50%) credible interval scaled with respect to the true distance is 0.85 (0.38). However, the chirp mass is well measured. Our chirp-mass estimates are subject to systematic error because we used gravitational-waveform templates without component spin to carry out inference on signals with moderate spins, but the total error is typically less than 10^(-3) M_☉. The median 90% (50%) credible region for sky localization is ~ 600 deg^2 (~150 deg^2), with 3% (30%) of detected events localized within 100 deg^2. Early aLIGO, with only two detectors, will have a sky-localization accuracy for binary neutron stars of hundreds of square degrees; this makes EM follow-up challenging, but not impossible
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
Rare mutations in SQSTM1 modify susceptibility to frontotemporal lobar degeneration
Mutations in the gene coding for Sequestosome 1 (SQSTM1) have been genetically associated with amyotrophic lateral sclerosis (ALS) and Paget disease of bone. In the present study, we analyzed the SQSTM1 coding sequence for mutations in an extended cohort of 1,808 patients with frontotemporal lobar degeneration (FTLD), ascertained within the European Early-Onset Dementia consortium. As control dataset, we sequenced 1,625 European control individuals and analyzed whole-exome sequence data of 2,274 German individuals (total n = 3,899). Association of rare SQSTM1 mutations was calculated in a meta-analysis of 4,332 FTLD and 10,240 control alleles. We identified 25 coding variants in FTLD patients of which 10 have not been described. Fifteen mutations were absent in the control individuals (carrier frequency < 0.00026) whilst the others were rare in both patients and control individuals. When pooling all variants with a minor allele frequency < 0.01, an overall frequency of 3.2 % was calculated in patients. Rare variant association analysis between patients and controls showed no difference over the whole protein, but suggested that rare mutations clustering in the UBA domain of SQSTM1 may influence disease susceptibility by doubling the risk for FTLD (RR = 2.18 [95 % CI 1.24-3.85]; corrected p value = 0.042). Detailed histopathology demonstrated that mutations in SQSTM1 associate with widespread neuronal and glial phospho-TDP-43 pathology. With this study, we provide further evidence for a putative role of rare mutations in SQSTM1 in the genetic etiology of FTLD and showed that, comparable to other FTLD/ALS genes, SQSTM1 mutations are associated with TDP-43 pathology
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