6,517 research outputs found
Suitability of post-Newtonian/numerical-relativity hybrid waveforms for gravitational wave detectors
This article presents a study of the sufficient accuracy of post-Newtonian
and numerical relativity waveforms for the most demanding usage case: parameter
estimation of strong sources in advanced gravitational wave detectors. For
black hole binaries, these detectors require accurate waveform models which can
be constructed by fusing an analytical post-Newtonian inspiral waveform with a
numerical relativity merger-ringdown waveform. We perform a comprehensive
analysis of errors that enter such "hybrid waveforms". We find that the
post-Newtonian waveform must be aligned with the numerical relativity waveform
to exquisite accuracy, about 1/100 of a gravitational wave cycle. Phase errors
in the inspiral phase of the numerical relativity simulation must be controlled
to less than about 0.1rad. (These numbers apply to moderately optimistic
estimates about the number of GW sources; exceptionally strong signals require
even smaller errors.) The dominant source of error arises from the inaccuracy
of the investigated post-Newtonian Taylor-approximants. Using our error
criterium, even at 3.5-th post-Newtonian order, hybridization has to be
performed significantly before the start of the longest currently available
numerical waveforms which cover 30 gravitational wave cycles. The current
investigation is limited to the equal-mass, zero-spin case and does not take
into account calibration errors of the gravitational wave detectors.Comment: 32 pages, 12 figures, submitted to CQG for the NRDA2010 conference
proceedings, added new figure (fig. 5) since last versio
Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation
Rapid Serial Visual Presentation (RSVP) is a paradigm that supports the
application of cortically coupled computer vision to rapid image search. In
RSVP, images are presented to participants in a rapid serial sequence which can
evoke Event-related Potentials (ERPs) detectable in their Electroencephalogram
(EEG). The contemporary approach to this problem involves supervised spatial
filtering techniques which are applied for the purposes of enhancing the
discriminative information in the EEG data. In this paper we make two primary
contributions to that field: 1) We propose a novel spatial filtering method
which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we
provide a comprehensive comparison of nine spatial filtering pipelines using
three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern
(CSP) and three linear classification methods Linear Discriminant Analysis
(LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR). Three
pipelines without spatial filtering are used as baseline comparison. The Area
Under Curve (AUC) is used as an evaluation metric in this paper. The results
reveal that MTWLB and xDAWN spatial filtering techniques enhance the
classification performance of the pipeline but CSP does not. The results also
support the conclusion that LR can be effective for RSVP based BCI if
discriminative features are available
Simulating the WFIRST coronagraph Integral Field Spectrograph
A primary goal of direct imaging techniques is to spectrally characterize the
atmospheres of planets around other stars at extremely high contrast levels. To
achieve this goal, coronagraphic instruments have favored integral field
spectrographs (IFS) as the science cameras to disperse the entire search area
at once and obtain spectra at each location, since the planet position is not
known a priori. These spectrographs are useful against confusion from speckles
and background objects, and can also help in the speckle subtraction and
wavefront control stages of the coronagraphic observation. We present a
software package, the Coronagraph and Rapid Imaging Spectrograph in Python
(crispy) to simulate the IFS of the WFIRST Coronagraph Instrument (CGI). The
software propagates input science cubes using spatially and spectrally resolved
coronagraphic focal plane cubes, transforms them into IFS detector maps and
ultimately reconstructs the spatio-spectral input scene as a 3D datacube.
Simulated IFS cubes can be used to test data extraction techniques, refine
sensitivity analyses and carry out design trade studies of the flight CGI-IFS
instrument. crispy is a publicly available Python package and can be adapted to
other IFS designs.Comment: 15 page
Inferring Core-Collapse Supernova Physics with Gravitational Waves
Stellar collapse and the subsequent development of a core-collapse supernova
explosion emit bursts of gravitational waves (GWs) that might be detected by
the advanced generation of laser interferometer gravitational-wave
observatories such as Advanced LIGO, Advanced Virgo, and LCGT. GW bursts from
core-collapse supernovae encode information on the intricate multi-dimensional
dynamics at work at the core of a dying massive star and may provide direct
evidence for the yet uncertain mechanism driving supernovae in massive stars.
Recent multi-dimensional simulations of core-collapse supernovae exploding via
the neutrino, magnetorotational, and acoustic explosion mechanisms have
predicted GW signals which have distinct structure in both the time and
frequency domains. Motivated by this, we describe a promising method for
determining the most likely explosion mechanism underlying a hypothetical GW
signal, based on Principal Component Analysis and Bayesian model selection.
Using simulated Advanced LIGO noise and assuming a single detector and linear
waveform polarization for simplicity, we demonstrate that our method can
distinguish magnetorotational explosions throughout the Milky Way (D <~ 10kpc)
and explosions driven by the neutrino and acoustic mechanisms to D <~ 2kpc.
Furthermore, we show that we can differentiate between models for rotating
accretion-induced collapse of massive white dwarfs and models of rotating iron
core collapse with high reliability out to several kpc.Comment: 22 pages, 9 figure
Improved methods for detecting gravitational waves associated with short gamma-ray bursts
In the era of second generation ground-based gravitational wave detectors,
short gamma-ray bursts (GRBs) will be among the most promising astrophysical
events for joint electromagnetic and gravitational wave observation. A targeted
search for gravitational wave compact binary merger signals in coincidence with
short GRBs was developed and used to analyze data from the first generation
LIGO and Virgo instruments. In this paper, we present improvements to this
search that enhance our ability to detect gravitational wave counterparts to
short GRBs. Specifically, we introduce an improved method for estimating the
gravitational wave background to obtain the event significance required to make
detections; implement a method of tiling extended sky regions, as required when
searching for signals associated to poorly localized GRBs from Fermi Gamma-ray
Burst Monitor or the InterPlanetary Network; and incorporate astrophysical
knowledge about the beaming of GRB emission to restrict the search parameter
space. We describe the implementation of these enhancements and demonstrate how
they improve the ability to observe binary merger gravitational wave signals
associated with short GRBs.Comment: 13 pages, 6 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
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