arXiv subjects

Colm Talbot

Publications and source records attributed to Colm Talbot.

43 records · Page 3Linked to original sources

arXiv2019

Thanks for the memory: measuring gravitational-wave memory in the first LIGO/Virgo gravitational-wave transient catalog

Gravitational-wave memory, a strong-field effect of general relativity, manifests itself as a permanent displacement in spacetime. We develop a Bayesian framework to detect gravitational-wave memory with the Advanced LIGO/Virgo detector network. We apply this algorithm on the ten binary black hole mergers in LIGO/Virgo's first transient gravitational-wave catalog. We find no evidence of memory, which is consistent with expectations. In order to estimate when memory will be detected, we use the best current population estimates to construct a realistic sample of binary black hole observations for LIGO/Virgo at design sensitivity. We show that an ensemble of $\mathcal{O}(2000)$ binary black hole observations can be used to find definitive evidence for gravitational-wave memory. We conclude that memory is likely to be detected in the early A+/Virgo+ era.

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arXiv2019

Higher order gravitational-wave modes with likelihood reweighting

The gravitational waveform of a merging stellar-mass binary is described at leading order by a quadrupolar mode. However, the complete waveform includes higher-order modes, which encode valuable information not accessible from the leading-order mode alone. Despite this, the majority of astrophysical inferences so far obtained with observations of gravitational waves employ only the leading order mode because calculations with higher-order modes are often computationally challenging. We show how to efficiently incorporate higher-order modes into astrophysical inference calculations with a two step procedure. First, we carry out Bayesian parameter estimation using a computationally cheap leading-order-mode waveform, which provides an initial estimate of binary parameters. Second, we weight the initial estimate using higher-order mode waveforms in order to fold in the extra information from the full waveform. We use mock data to demonstrate the effectiveness of this method. We apply the method to each binary black hole event in the first gravitational-wave transient catalog GWTC-1 to obtain posterior distributions and Bayesian evidence with higher-order modes. Performing Bayesian model selection on the events in GWTC-1, we find only a weak preference for waveforms with higher order modes. We discuss how this method can be generalized to a variety of other applications.

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arXiv2019

Parallelized Inference for Gravitational-Wave Astronomy

Bayesian inference is the workhorse of gravitational-wave astronomy, for example, determining the mass and spins of merging black holes, revealing the neutron star equation of state, and unveiling the population properties of compact binaries. The science enabled by these inferences comes with a computational cost that can limit the questions we are able to answer. This cost is expected to grow. As detectors improve, the detection rate will go up, allowing less time to analyze each event. Improvement in low-frequency sensitivity will yield longer signals, increasing the number of computations per event. The growing number of entries in the transient catalog will drive up the cost of population studies. While Bayesian inference calculations are not entirely parallelizable, key components are embarrassingly parallel: calculating the gravitational waveform and evaluating the likelihood function. Graphical processor units (GPUs) are adept at such parallel calculations. We report on progress porting gravitational-wave inference calculations to GPUs. Using a single code - which takes advantage of GPU architecture if it is available - we compare computation times using modern GPUs (NVIDIA P100) and CPUs (Intel Gold 6140). We demonstrate speed-ups of $\sim 50 \times$ for compact binary coalescence gravitational waveform generation and likelihood evaluation and more than $100\times$ for population inference within the lifetime of current detectors. Further improvement is likely with continued development. Our python-based code is publicly available and can be used without familiarity with the parallel computing platform, CUDA.

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arXiv2018

Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy

Bayesian parameter estimation is fast becoming the language of gravitational-wave astronomy. It is the method by which gravitational-wave data is used to infer the sources' astrophysical properties. We introduce a user-friendly Bayesian inference library for gravitational-wave astronomy, Bilby. This python code provides expert-level parameter estimation infrastructure with straightforward syntax and tools that facilitate use by beginners. It allows users to perform accurate and reliable gravitational-wave parameter estimation on both real, freely-available data from LIGO/Virgo, and simulated data. We provide a suite of examples for the analysis of compact binary mergers and other types of signal model including supernovae and the remnants of binary neutron star mergers. These examples illustrate how to change the signal model, how to implement new likelihood functions, and how to add new detectors. Bilby has additional functionality to do population studies using hierarchical Bayesian modelling. We provide an example in which we infer the shape of the black hole mass distribution from an ensemble of observations of binary black hole mergers.

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arXiv2018

Gravitational-wave memory: waveforms and phenomenology

The non-linear gravitational-wave memory effect is a prediction of general relativity in which test masses are permanently displaced by gravitational radiation. We implement a method for calculating the expected memory waveform from an oscillatory gravitational-wave time series. We use this method to explore the phenomenology of gravitational-wave memory using a numerical relativity surrogate model. Previous methods of calculating the memory have considered only the dominant oscillatory ($\ell=2$, $m=|2|$) mode in the spherical harmonic decomposition or the post-Newtonian expansion. We explore the contribution of higher-order modes and reveal a richer phenomenology than is apparent with $\ell=|m|=2$ modes alone. We also consider the `memory of the memory' in which the memory is, itself, a source of memory, which leads to a small, $O\left(10^{-4}\right)$, correction to the memory waveform. The method is implemented in the python package {\tt\sc GWMemory}, which is made publicly available.

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arXiv2018

Measuring the binary black hole mass spectrum with an astrophysically motivated parameterization

Gravitational-wave detections have revealed a previously unknown population of stellar mass black holes with masses above $20\, M_{\odot}$. These observations provide a new way to test models of stellar evolution for massive stars. By considering the astrophysical processes likely to determine the shape of the binary black hole mass spectrum, we construct a parameterized model to capture key spectral features that relate gravitational-wave data to theoretical stellar astrophysics. In particular, we model the signature of pulsational pair-instability supernovae, which are expected to cause all stars with initial mass $100\, M_{\odot}\lesssim M \lesssim 150\, M_{\odot}$ to form $\sim 40\, M_{\odot}$ black holes. This would cause a cut-off in the black hole mass spectrum along with an excess of black holes near $40\, M_{\odot}$. We carry out a simulated data study to illustrate some of the stellar physics that can be inferred using gravitational-wave measurements of binary black holes and demonstrate several such inferences that might be made in the near future. First, we measure the minimum and maximum stellar black hole mass. Second, we infer the presence of a peak due to pair-instability supernovae. Third, we measure the black hole mass ratio distribution. Finally, we show how inadequate models of the black hole mass spectrum lead to biased estimates of the merger rate and the amplitude of the stochastic gravitational-wave background.

astro-ph.HE↗

arXiv2017

Determining the population properties of spinning black holes

There are at least two formation scenarios consistent with the first gravitational-wave observations of binary black hole mergers. In field models, black hole binaries are formed from stellar binaries that may undergo common envelope evolution. In dynamic models, black hole binaries are formed through capture events in globular clusters. Both classes of models are subject to significant theoretical uncertainties. Nonetheless, the conventional wisdom holds that the distribution of spin orientations of dynamically merging black holes is nearly isotropic while field-model black holes prefer to spin in alignment with the orbital angular momentum. We present a framework in which observations of black hole mergers can be used to measure ensemble properties of black hole spin such as the typical black hole spin misalignment. We show how to obtain constraints on population hyperparameters using minimal assumptions so that the results are not strongly dependent on the uncertain physics of formation models. These data-driven constraints will facilitate tests of theoretical models and help determine the formation history of binary black holes using information encoded in their observed spins. We demonstrate that the ensemble properties of binary detections can be used to search for and characterize the properties of two distinct populations of black hole mergers.

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