26,724 research outputs found
A Bayesian Approach to the Detection Problem in Gravitational Wave Astronomy
The analysis of data from gravitational wave detectors can be divided into
three phases: search, characterization, and evaluation. The evaluation of the
detection - determining whether a candidate event is astrophysical in origin or
some artifact created by instrument noise - is a crucial step in the analysis.
The on-going analyses of data from ground based detectors employ a frequentist
approach to the detection problem. A detection statistic is chosen, for which
background levels and detection efficiencies are estimated from Monte Carlo
studies. This approach frames the detection problem in terms of an infinite
collection of trials, with the actual measurement corresponding to some
realization of this hypothetical set. Here we explore an alternative, Bayesian
approach to the detection problem, that considers prior information and the
actual data in hand. Our particular focus is on the computational techniques
used to implement the Bayesian analysis. We find that the Parallel Tempered
Markov Chain Monte Carlo (PTMCMC) algorithm is able to address all three phases
of the anaylsis in a coherent framework. The signals are found by locating the
posterior modes, the model parameters are characterized by mapping out the
joint posterior distribution, and finally, the model evidence is computed by
thermodynamic integration. As a demonstration, we consider the detection
problem of selecting between models describing the data as instrument noise, or
instrument noise plus the signal from a single compact galactic binary. The
evidence ratios, or Bayes factors, computed by the PTMCMC algorithm are found
to be in close agreement with those computed using a Reversible Jump Markov
Chain Monte Carlo algorithm.Comment: 19 pages, 12 figures, revised to address referee's comment
Use of the MultiNest algorithm for gravitational wave data analysis
We describe an application of the MultiNest algorithm to gravitational wave
data analysis. MultiNest is a multimodal nested sampling algorithm designed to
efficiently evaluate the Bayesian evidence and return posterior probability
densities for likelihood surfaces containing multiple secondary modes. The
algorithm employs a set of live points which are updated by partitioning the
set into multiple overlapping ellipsoids and sampling uniformly from within
them. This set of live points climbs up the likelihood surface through nested
iso-likelihood contours and the evidence and posterior distributions can be
recovered from the point set evolution. The algorithm is model-independent in
the sense that the specific problem being tackled enters only through the
likelihood computation, and does not change how the live point set is updated.
In this paper, we consider the use of the algorithm for gravitational wave data
analysis by searching a simulated LISA data set containing two non-spinning
supermassive black hole binary signals. The algorithm is able to rapidly
identify all the modes of the solution and recover the true parameters of the
sources to high precision.Comment: 18 pages, 4 figures, submitted to Class. Quantum Grav; v2 includes
various changes in light of referee's comment
Separating Gravitational Wave Signals from Instrument Artifacts
Central to the gravitational wave detection problem is the challenge of
separating features in the data produced by astrophysical sources from features
produced by the detector. Matched filtering provides an optimal solution for
Gaussian noise, but in practice, transient noise excursions or ``glitches''
complicate the analysis. Detector diagnostics and coincidence tests can be used
to veto many glitches which may otherwise be misinterpreted as gravitational
wave signals. The glitches that remain can lead to long tails in the matched
filter search statistics and drive up the detection threshold. Here we describe
a Bayesian approach that incorporates a more realistic model for the instrument
noise allowing for fluctuating noise levels that vary independently across
frequency bands, and deterministic ``glitch fitting'' using wavelets as
``glitch templates'', the number of which is determined by a trans-dimensional
Markov chain Monte Carlo algorithm. We demonstrate the method's effectiveness
on simulated data containing low amplitude gravitational wave signals from
inspiraling binary black hole systems, and simulated non-stationary and
non-Gaussian noise comprised of a Gaussian component with the standard
LIGO/Virgo spectrum, and injected glitches of various amplitude, prevalence,
and variety. Glitch fitting allows us to detect significantly weaker signals
than standard techniques.Comment: 21 pages, 18 figure
Scalable Solutions for Automated Single Pulse Identification and Classification in Radio Astronomy
Data collection for scientific applications is increasing exponentially and
is forecasted to soon reach peta- and exabyte scales. Applications which
process and analyze scientific data must be scalable and focus on execution
performance to keep pace. In the field of radio astronomy, in addition to
increasingly large datasets, tasks such as the identification of transient
radio signals from extrasolar sources are computationally expensive. We present
a scalable approach to radio pulsar detection written in Scala that
parallelizes candidate identification to take advantage of in-memory task
processing using Apache Spark on a YARN distributed system. Furthermore, we
introduce a novel automated multiclass supervised machine learning technique
that we combine with feature selection to reduce the time required for
candidate classification. Experimental testing on a Beowulf cluster with 15
data nodes shows that the parallel implementation of the identification
algorithm offers a speedup of up to 5X that of a similar multithreaded
implementation. Further, we show that the combination of automated multiclass
classification and feature selection speeds up the execution performance of the
RandomForest machine learning algorithm by an average of 54% with less than a
2% average reduction in the algorithm's ability to correctly classify pulsars.
The generalizability of these results is demonstrated by using two real-world
radio astronomy data sets.Comment: In Proceedings of the 47th International Conference on Parallel
Processing (ICPP 2018). ACM, New York, NY, USA, Article 11, 11 page
When Darwin Met Einstein: Gravitational Lens Inversion with Genetic Algorithms
Gravitational lensing can magnify a distant source, revealing structural
detail which is normally unresolvable. Recovering this detail through an
inversion of the influence of gravitational lensing, however, requires
optimisation of not only lens parameters, but also of the surface brightness
distribution of the source. This paper outlines a new approach to this
inversion, utilising genetic algorithms to reconstruct the source profile. In
this initial study, the effects of image degradation due to instrumental and
atmospheric effects are neglected and it is assumed that the lens model is
accurately known, but the genetic algorithm approach can be incorporated into
more general optimisation techniques, allowing the optimisation of both the
parameters for a lensing model and the surface brightness of the source.Comment: 9 pages, to appear in PAS
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