arXiv subjects

Neil J. Cornish

Publications and source records attributed to Neil J. Cornish.

At least 109 records · Page 6Linked to original sources

arXiv2015

Enabling high confidence detections of gravitational-wave bursts

With the advanced LIGO and Virgo detectors taking observations the detection of gravitational waves is expected within the next few years. Extracting astrophysical information from gravitational wave detections is a well-posed problem and thoroughly studied when detailed models for the waveforms are available. However, one motivation for the field of gravitational wave astronomy is the potential for new discoveries. Recognizing and characterizing unanticipated signals requires data analysis techniques which do not depend on theoretical predictions for the gravitational waveform. Past searches for short-duration un-modeled gravitational wave signals have been hampered by transient noise artifacts, or "glitches," in the detectors. In some cases, even high signal-to-noise simulated astrophysical signals have proven difficult to distinguish from glitches, so that essentially any plausible signal could be detected with at most 2-3 $σ$ level confidence. We have put forth the BayesWave algorithm to differentiate between generic gravitational wave transients and glitches, and to provide robust waveform reconstruction and characterization of the astrophysical signals. Here we study BayesWave's capabilities for rejecting glitches while assigning high confidence to detection candidates through analytic approximations to the Bayesian evidence. Analytic results are tested with numerical experiments by adding simulated gravitational wave transient signals to LIGO data collected between 2009 and 2010 and found to be in good agreement.

gr-qc↗

arXiv2015

When is a gravitational-wave signal stochastic?

We discuss the detection of gravitational-wave backgrounds in the context of Bayesian inference and suggest a practical definition of what it means for a signal to be considered stochastic---namely, that the Bayesian evidence favors a stochastic signal model over a deterministic signal model. A signal can further be classified as Gaussian-stochastic if a Gaussian signal model is favored. In our analysis we use Bayesian model selection to choose between several signal and noise models for simulated data consisting of uncorrelated Gaussian detector noise plus a superposition of sinusoidal signals from an astrophysical population of gravitational-wave sources. For simplicity, we consider co-located and co-aligned detectors with white detector noise, but the method can be extended to more realistic detector configurations and power spectra. The general trend we observe is that a deterministic model is favored for small source numbers, a non-Gaussian stochastic model is preferred for intermediate source numbers, and a Gaussian stochastic model is preferred for large source numbers. However, there is very large variation between individual signal realizations, leading to fuzzy boundaries between the three regimes. We find that a hybrid, trans-dimensional model comprised of a deterministic signal model for individual bright sources and a Gaussian-stochastic signal model for the remaining confusion background outperforms all other models in most instances.

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arXiv2015

Phase-coherent mapping of gravitational-wave backgrounds using ground-based laser interferometers

We extend the formalisms developed in Gair et al. and Cornish and van Haasteren to create maps of gravitational-wave backgrounds using a network of ground-based laser interferometers. We show that in contrast to pulsar timing arrays, which are insensitive to half of the gravitational-wave sky (the curl modes), a network of ground-based interferometers is sensitive to both the gradient and curl components of the background. The spatial separation of a network of interferometers, or of a single interferometer at different times during its rotational and orbital motion around the Sun, allows for recovery of both components. We derive expressions for the response functions of a laser interferometer in the small-antenna limit, and use these expressions to calculate the overlap reduction function for a pair of interferometers. We also construct maximum-likelihood estimates of the + and x-polarization modes of the gravitational-wave sky in terms of the response matrix for a network of ground-based interferometers, evaluated at discrete times during Earth's rotational and orbital motion around the Sun. We demonstrate the feasibility of this approach for some simple simulated backgrounds (a single point source and spatially-extended distributions having only grad or curl components), calculating maximum-likelihood sky maps and uncertainty maps based on the (pseudo)inverse of the response matrix. The distinction between this approach and standard methods for mapping gravitational-wave power is also discussed.

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arXiv2015

BayesWave: Bayesian Inference for Gravitational Wave Bursts and Instrument Glitches

A central challenge in Gravitational Wave Astronomy is identifying weak signals in the presence of non-stationary and non-Gaussian noise. The separation of gravitational wave signals from noise requires good models for both. When accurate signal models are available, such as for binary Neutron star systems, it is possible to make robust detection statements even when the noise is poorly understood. In contrast, searches for "un-modeled" transient signals are strongly impacted by the methods used to characterize the noise. Here we take a Bayesian approach and introduce a multi-component, variable dimension, parameterized noise model that explicitly accounts for non-stationarity and non-Gaussianity in data from interferometric gravitational wave detectors. Instrumental transients (glitches) and burst sources of gravitational waves are modeled using a Morlet-Gabor continuous wavelet frame. The number and placement of the wavelets is determined by a trans-dimensional Reversible Jump Markov Chain Monte Carlo algorithm. The Gaussian component of the noise and sharp line features in the noise spectrum are modeled using the BayesLine algorithm, which operates in concert with the wavelet model.

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arXiv2015

Constraining the Solution to the Last Parsec Problem with Pulsar Timing

The detection of a stochastic gravitational-wave signal from the superposition of many inspiraling supermassive black holes with pulsar timing arrays (PTAs) is likely to occur within the next decade. With this detection will come the opportunity to learn about the processes that drive black-hole-binary systems toward merger through their effects on the gravitational-wave spectrum. We use Bayesian methods to investigate the extent to which effects other than gravitational-wave emission can be distinguished using PTA observations. We show that, even in the absence of a detection, it is possible to place interesting constraints on these dynamical effects for conservative predictions of the population of tightly bound supermassive black-hole binaries. For instance, if we assume a relatively weak signal consistent with a low number of bound binaries and a low black-hole-mass to galaxy-mass correlation, we still find that a non-detection by a simulated array, with a sensitivity that should be reached in practice within a few years, disfavors gravitational-wave-dominated evolution with an odds ratio of $\sim$30:1. Such a finding would suggest either that all existing astrophysical models for the population of tightly bound binaries are overly optimistic, or else that some dynamical effect other than gravitational-wave emission is actually dominating binary evolution even at the relatively high frequencies/small orbital separations probed by PTAs.

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arXiv2015

Fisher vs. Bayes : A comparison of parameter estimation techniques for massive black hole binaries to high redshifts with eLISA

Massive black hole binaries are the primary source of gravitational waves (GW) for the future eLISA observatory. The detection and parameter estimation of these sources to high redshift would provide invaluable information on the formation mechanisms of seed black holes, and on the evolution of massive black holes and their host galaxies through cosmic time. The Fisher information matrix has been the standard tool for GW parameter estimation in the last two decades. However, recent studies have questioned the validity of using the Fisher matrix approach. For example, the Fisher matrix approach sometimes predicts errors of $\geq100\%$ in the estimation of parameters such as the luminosity distance and sky position. With advances in computing power, Bayesian inference is beginning to replace the Fisher matrix approximation in parameter estimation studies. In this work, we conduct a Bayesian inference analysis for 120 sources situated at redshifts of between $0.1\leq z\leq 13.2$, and compare the results with those from a Fisher matrix analysis. The Fisher matrix results suggest that for this particular selection of sources, eLISA would be unable to localize sources at redshifts of $z\lesssim6$. In contrast, Bayesian inference provides finite error estimations for all sources in the study, and shows that we can establish minimum closest distances for all sources. The study further predicts that we should be capable with eLISA, out to a redshift of at least $z\leq13$, of predicting a maximum error in the chirp mass of $\lesssim 1\%$, the reduced mass of $\lesssim20\%$, the time to coalescence of 2 hours, and to a redshift of $z\sim5$, the inclination of the source with a maximum error of $\sim60$ degrees.

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arXiv2014

Multivariate Classification with Random Forests for Gravitational Wave Searches of Black Hole Binary Coalescence

Searches for gravitational waves produced by coalescing black hole binaries with total masses $\gtrsim25\,$M$_\odot$ use matched filtering with templates of short duration. Non-Gaussian noise bursts in gravitational wave detector data can mimic short signals and limit the sensitivity of these searches. Previous searches have relied on empirically designed statistics incorporating signal-to-noise ratio and signal-based vetoes to separate gravitational wave candidates from noise candidates. We report on sensitivity improvements achieved using a multivariate candidate ranking statistic derived from a supervised machine learning algorithm. We apply the random forest of bagged decision trees technique to two separate searches in the high mass $\left( \gtrsim25\,\mathrm{M}_\odot \right)$ parameter space. For a search which is sensitive to gravitational waves from the inspiral, merger, and ringdown (IMR) of binary black holes with total mass between $25\,$M$_\odot$ and $100\,$M$_\odot$, we find sensitive volume improvements as high as $70_{\pm 13}-109_{\pm 11}$\% when compared to the previously used ranking statistic. For a ringdown-only search which is sensitive to gravitational waves from the resultant perturbed intermediate mass black hole with mass roughly between $10\,$M$_\odot$ and $600\,$M$_\odot$, we find sensitive volume improvements as high as $61_{\pm 4}-241_{\pm 12}$\% when compared to the previously used ranking statistic. We also report how sensitivity improvements can differ depending on mass regime, mass ratio, and available data quality information. Finally, we describe the techniques used to tune and train the random forest classifier that can be generalized to its use in other searches for gravitational waves.

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arXiv2014

BayesLine: Bayesian Inference for Spectral Estimation of Gravitational Wave Detector Noise

Gravitational wave data from ground-based detectors is dominated by instrument noise. Signals will be comparatively weak, and our understanding of the noise will influence detection confidence and signal characterization. Mis-modeled noise can produce large systematic biases in both model selection and parameter estimation. Here we introduce a multi-component, variable dimension, parameterized model to describe the Gaussian-noise power spectrum for data from ground-based gravitational wave interferometers. Called BayesLine, the algorithm models the noise power spectral density using cubic splines for smoothly varying broad-band noise and Lorentzians for narrow-band line features in the spectrum. We describe the algorithm and demonstrate its performance on data from the fifth and sixth LIGO science runs. Once fully integrated into LIGO/Virgo data analysis software, BayesLine will produce accurate spectral estimation and provide a means for marginalizing inferences drawn from the data over all plausible noise spectra.

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arXiv2014

Mapping the nano-Hertz gravitational wave sky

We describe a new method for extracting gravitational wave signals from pulsar timing data. We show that any gravitational wave signal can be decomposed into an orthogonal set of sky maps, with the number of maps equal to the number of pulsars in the timing array. These maps may be used as a basis to construct gravitational wave templates for any type of source, including collections of point sources. A variant of the standard Hellings-Downs correlation analysis is recovered for statistically isotropic signals. The template based approach allows us to probe potential anisotropies in the signal and produce maps of the gravitational wave sky.

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arXiv2013

Detecting a Stochastic Gravitational Wave Background in the presence of a Galactic Foreground and Instrument Noise

Detecting a stochastic gravitational wave background requires that we first understand and model any astrophysical foregrounds. In the millihertz frequency band, the predominate foreground signal will be from unresolved white dwarf binaries in the galaxy. We build on our previous work to show that a stochastic gravitational wave background can be detected in the presence of both instrument noise and a galactic confusion foreground. The key to our approach is accurately modeling the spectra for each of the various signal components. We simulate data for a gigameter Laser Interferometer Space Antenna (LISA) operating in the mHz frequency band detector operating with both 6- and 4-links. We obtain posterior distribution functions for the instrument noise parameters, the galaxy level and modulation parameters, and the stochastic background energy density. We find that we are able to detect a scale-invariant stochastic background with energy density as low as Omega_gw = 2e-13 for a 6-link interferometer and Omega_gw = 5e-13 for a 4-link interferometer with one year of data.

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arXiv2013

Towards a unified treatment of gravitational-wave data analysis

We present a unified description of gravitational-wave data analysis that unites the template-based analysis used to detect deterministic signals from well-modeled sources, such as binary-black-hole mergers, with the cross-correlation analysis used to detect stochastic gravitational-wave backgrounds. We also discuss the connection between template-based analyses and those that target poorly-modeled bursts of gravitational waves, and suggest a new approach for detecting burst signals.

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arXiv2013

Pulsar Timing Array Analysis for Black Hole Backgrounds

An astrophysical population of supermassive black hole binaries is thought to be the strongest source of gravitational waves in the frequency range covered by Pulsar Timing Arrays (PTAs). A potential cause for concern is that the standard cross-correlation method used in PTA data analysis assumes that the signals are isotropically distributed and Gaussian random, while the signals from a black hole population are likely to be anisotropic and deterministic. Here we argue that while the conventional analysis is not optimal, it is not hopeless either, as the standard Hellings-Downs correlation curve turns out to hold for point sources, and the small effective number of signal samples blurs the distinction between Gaussian and deterministic signals. Possible improvements to the standard cross-correlation analysis that account for the anisotropy of the signal are discussed.

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arXiv2013

Fast Fisher Matrices and Lazy Likelihoods

Theoretical studies in gravitational wave astronomy often require the calculation of Fisher Information Matrices and Likelihood functions, which in a direct approach entail the costly step of computing gravitational waveforms. Here I describe an alternative technique that sidesteps the need to compute full waveforms, resulting in significant computational savings. I describe how related techniques can be used to speed up Bayesian inference applied to real gravitational wave data.

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arXiv2012

Applying Bayesian Inference to the first International Pulsar Timing Array data challenge

This is a very brief summary of the techniques I used to analyze the IPTA challenge 1 data sets. I tried many things, and more failed than succeeded, but in the end I found two approaches that appear to work based on tests done using the open data sets. One approach works directly with the time domain data, and the other works with a specially constructed Fourier transform of the data. The raw data was run through TEMPO2 to produce reduced timing residuals for the analysis. Standard Markov Chain Monte Carlo techniques were used to produce samples from the posterior distribution function for the model parameters. The model parameters include the gravitational wave amplitude and spectral slope, and the white noise amplitude for each pulsar in the array. While red timing noise was only included in Dataset 3, I found that it was necessary to include effective red noise in all the analyses to account for some of the spurious effects introduced by the TEMPO2 timing fit. This added an additional amplitude and slope parameter for each pulsar, so my overall model for the 36 pulsars residuals has 110 parameters. As an alternative to using an effective red noise model, I also tried to simultaneously re-fit the timing model model while looking for the gravitational wave signal, but for reasons that are not yet clear, this approach was not very successful. I comment briefly on ways in which the algorithms could be improved. My best estimates for the gravitational wave amplitudes in the three closed (blind) data sets are: (1) $A=(7.3\pm 1.0)\times 10^{-15}$; (2) $A=(5.7\pm 0.6)\times 10^{-14}$; and (3) $A=(4.6\pm 1.3)\times 10^{-15}$.

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arXiv2012

Low-frequency gravitational-wave science with eLISA/NGO

We review the expected science performance of the New Gravitational-Wave Observatory (NGO, a.k.a. eLISA), a mission under study by the European Space Agency for launch in the early 2020s. eLISA will survey the low-frequency gravitational-wave sky (from 0.1 mHz to 1 Hz), detecting and characterizing a broad variety of systems and events throughout the Universe, including the coalescences of massive black holes brought together by galaxy mergers; the inspirals of stellar-mass black holes and compact stars into central galactic black holes; several millions of ultracompact binaries, both detached and mass transferring, in the Galaxy; and possibly unforeseen sources such as the relic gravitational-wave radiation from the early Universe. eLISA's high signal-to-noise measurements will provide new insight into the structure and history of the Universe, and they will test general relativity in its strong-field dynamical regime.

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arXiv2012

Prospects for observing ultra-compact binaries with space-based gravitational wave interferometers and optical telescopes

Space-based gravitational wave interferometers are sensitive to the galactic population of ultra-compact binaries. An important subset of the ultra-compact binary population are those stars that can be individually resolved by both gravitational wave interferometers and electromagnetic telescopes. The aim of this paper is to quantify the multi-messenger potential of space-based interferometers with arm-lengths between 1 and 5 Gm. The Fisher Information Matrix is used to estimate the number of binaries from a model of the Milky Way which are localized on the sky by the gravitational wave detector to within 1 and 10 square degrees and bright enough to be detected by a magnitude limited survey. We find, depending on the choice of GW detector characteristics, limiting magnitude, and observing strategy, that up to several hundred gravitational wave sources could be detected in electromagnetic follow-up observations.

astro-ph.IM↗

arXiv2012

Constraints on the Topology of the Universe: Extension to General Geometries

We present an update to the search for a non-trivial topology of the universe by searching for matching circle pairs in the cosmic microwave background using the WMAP 7 year data release. We extend the exisiting bounds to encompass a wider range of possible topologies by searching for matching circle pairs with opening angles 10 degree < α< 90 degree and separation angles 11 degree < θ< 180 degree. The extended search reveal two small anomalous regions in the CMB sky. Numerous pairs of well-matched circles are found where both circles pass through one or the other of those regions. As this is not the signature of any known manifold, but is a likely consequence of contamination in those sky regions, we repeat the search excluding circle pairs where both pass through either of the two regions. We then find no statistically significant pairs of matched circles, and so no hints of a non-trivial topology. The absence of matched circles increases the lower limit on the length of the shortest closed null geodesic that self-intersects at our location in the universe (equivalently the injectivity radius at our location) to 98.5% of the diameter of the last scattering surface or approximately 26 Gpc. It extends the limit to any manifolds in which the intersecting arcs of said geodesic form an angle greater than 10^o.

astro-ph.CO↗

arXiv2012

Gravitational Wave Astronomy: Needle in a Haystack

A world-wide array of highly sensitive interferometers stands poised to usher in a new era in astronomy with the first direct detection of gravitational waves. The data from these instruments will provide a unique perspective on extreme astrophysical phenomena such as neutron stars and black holes, and will allow us to test Einstein's theory of gravity in the strong field, dynamical regime. To fully realize these goals we need to solve some challenging problems in signal processing and inference, such as finding rare and weak signals that are buried in non-stationary and non-Gaussian instrument noise, dealing with high-dimensional model spaces, and locating what are often extremely tight concentrations of posterior mass within the prior volume. Gravitational wave detection using space based detectors and Pulsar Timing Arrays bring with them the additional challenge of having to isolate individual signals that overlap one another in both time and frequency. Promising solutions to these problems will be discussed, along with some of the challenges that remain.

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