arXiv subjects

Christopher J. Moore

Publications and source records attributed to Christopher J. Moore.

60 records · Page 4Linked to original sources

arXiv2015

Testing the "no-hair" property of black holes with X-ray observations of accretion disks

Accretion disks around black holes radiate a significant fraction of the rest mass of the accreting material in the form of thermal radiation from within a few gravitational radii of the black hole ($ r \lesssim 20 G M / c^{2}$). In addition, the accreting matter may also be illuminated by hard X-rays from the surrounding plasma which adds fluorescent transition lines to the emission. This radiation is emitted by matter moving along geodesics in the metric, therefore the strong Doppler and gravitational redshifts observed in the emission encode information about the strong gravitational field around the black hole. In this paper the possibility of using the X-ray emission as a strong field test of General Relativity is explored by calculating the spectra for both the transition line and thermal emission from a thin accretion disk in a series of parametrically deformed Kerr metrics. In addition the possibility of constraining a number of known black hole spacetimes in alternative theories of gravity is considered.

gr-qc↗

arXiv2015

Quantifying and mitigating bias in inference on gravitational wave source populations

When using incorrect or inaccurate signal models to perform parameter estimation on a gravitational wave signal, biased parameter estimates will in general be obtained. For a single event this bias may be consistent with the posterior, but when considering a population of events this bias becomes evident as a sag below the expected diagonal line of the P-P plot showing the fraction of signals found within a certain significance level versus that significance level. It would be hoped that recently proposed techniques for accounting for model uncertainties in parameter estimation would, to some extent, alleviate this problem. Here we demonstrate that this is indeed the case. We derive an analytic approximation to the P-P plot obtained when using an incorrect signal model to perform parameter estimation. This approximation is valid in the limit of high signal-to-noise ratio and nearly correct waveform models. We show how the P-P plot changes if a Gaussian process likelihood that allows for model errors is used to analyse the data. We demonstrate analytically and using numerical simulations that the bias is always reduced in this way. These results provide a way to quantify bias in inference on populations and demonstrate the importance of utilising methods to mitigate this bias.

gr-qc↗

arXiv2015

Estimating the sensitivity of pulsar timing arrays

The sensitivity curve of a canonical pulsar timing array is calculated for two types of source: a monochromatic wave and a stochastic background. These calculations are performed in both a Bayesian and frequentist framework, using both analytical and numerical methods. These calculations are used to clarify the interpretation of the sensitivity curves and to illustrate the sometimes overlooked fact that the sensitivity curve depends not only on the properties of the pulse time-of-arrival data set but also on the properties of the source being observed. The Bayesian and frequentist frameworks were found to give consistent results and the analytic and numerical calculations were also found to be in good agreement.

astro-ph.IM↗

arXiv2014

Gravitational-wave sensitivity curves

There are several common conventions in use by the gravitational-wave community to describe the amplitude of sources and the sensitivity of detectors. These are frequently confused. We outline the merits of and differences between the various quantities used for parameterizing noise curves and characterizing gravitational-wave amplitudes. We conclude by producing plots that consistently compare different detectors. Similar figures can be generated on-line for general use at \url{http://rhcole.com/apps/GWplotter}.

gr-qc↗

arXiv2014

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.

gr-qc↗

arXiv2014

The sensitivity of pulsar timing arrays

Simple analytic expressions are derived for the sensitivity curve of a pulsar timing array (PTA) to both a monochromatic source of gravitational waves and an isotropic stochastic background of gravitational waves. These derivations are performed in both frequentist and Bayesian frameworks and the results verified with numerical injections and recovery in mock PTA datasets. The consistency of the frequentist and Bayesian approaches is demonstrated and the results are used to emphasise the fact that the sensitivity curve of a PTA depends not only on the properties of the PTA itself, but also on the properties of the gravitational wave source being observed.

gr-qc↗