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Register a new userAn improved analysis of GW150914 using a fully spin-precessing waveform model
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This paper presents updated estimates of source parameters for GW150914, a binary black-hole coalescence event detected by the Laser Interferometer Gravitational-wave Observatory (LIGO) on September 14, 2015 [1]. Reference presented parameter estimation [2] of the source using a 13-dimensional, phenomenological precessing-spin model (precessing IMRPhenom) and a 11-dimensional nonprecessing effective-one-body (EOB) model calibrated to numerical-relativity simulations, which forces spin alignment (nonprecessing EOBNR). Here we present new results that include a 15-dimensional precessing-spin waveform model (precessing EOBNR) developed within the EOB formalism. We find good agreement with the parameters estimated previously [2], and we quote updated component masses of $35^{+5}_{-3}mathrm{M}_odot$ and $30^{+3}_{-4}mathrm{M}_odot$ (where errors correspond to 90% symmetric credible intervals). We also present slightly tighter constraints on the dimensionless spin magnitudes of the two black holes, with a primary spin estimate $0.65$ and a secondary spin estimate $0.75$ at 90% probability. Reference [2] estimated the systematic parameter-extraction errors due to waveform-model uncertainty by combining the posterior probability densities of precessing IMRPhenom and nonprecessing EOBNR. Here we find that the two precessing-spin models are in closer agreement, suggesting that these systematic errors are smaller than previously quoted.
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The recent observation of GW190412, the first high-mass ratio binary black-hole (BBH) merger, by the LIGO-Virgo Collaboration (LVC) provides a unique opportunity to probe the impact of subdominant harmonics and precession effects encoded in a gravitational wave signal. We present refined estimates of source parameters for GW190412 using texttt{NRSur7dq4}, a recently developed numerical relativity waveform surrogate model that includes all $ell leq 4$ spin-weighted spherical harmonic modes as well as the full physical effects of precession. We compare our results with two different variants of phenomenological precessing BBH waveform models, texttt{IMRPhenomPv3HM} and texttt{IMRPhenomXPHM}, as well as to the LVC results. Our results are broadly in agreement with texttt{IMRPhenomXPHM} results and the reported LVC analysis compiled with the texttt{SEOBNRv4PHM} waveform model, but in tension with texttt{IMRPhenomPv3HM}. Using the texttt{NRSur7dq4} model, we provide a tighter constraint on the mass-ratio ($0.26^{+0.08}_{-0.06}$) as compared to the LVC estimate of $0.28^{+0.13}_{-0.07}$ (both reported as median values withs 90% credible intervals). We also constrain the binary to be more face-on, and find a broader posterior for the spin precession parameter. We further find that even though $ell=4$ harmonic modes have negligible signal-to-noise ratio, omission of these modes will influence the estimated posterior distribution of several source parameters including chirp mass, effective inspiral spin, luminosity distance, and inclination. We also find that commonly used model approximations, such as neglecting the asymmetric modes (which are generically excited during precession), have negligible impact on parameter recovery for moderate SNR-events similar to GW190412.
Parameter estimates of GW150914 were obtained using Bayesian inference, based on three semi-analytic waveform models for binary black hole coalescences. These waveform models differ from each other in their treatment of black hole spins, and all three models make some simplifying assumptions, notably to neglect sub-dominant waveform harmonic modes and orbital eccentricity. Furthermore, while the models are calibrated to agree with waveforms obtained by full numerical solutions of Einsteins equations, any such calibration is accurate only to some non-zero tolerance and is limited by the accuracy of the underlying phenomenology, availability, quality, and parameter-space coverage of numerical simulations. This paper complements the original analyses of GW150914 with an investigation of the effects of possible systematic errors in the waveform models on estimates of its source parameters. To test for systematic errors we repeat the original Bayesian analyses on mock signals from numerical simulations of a series of binary configurations with parameters similar to those found for GW150914. Overall, we find no evidence for a systematic bias relative to the statistical error of the original parameter recovery of GW150914 due to modeling approximations or modeling inaccuracies. However, parameter biases are found to occur for some configurations disfavored by the data of GW150914: for binaries inclined edge-on to the detector over a small range of choices of polarization angles, and also for eccentricities greater than $sim$0.05. For signals with higher signal-to-noise ratio than GW150914, or in other regions of the binary parameter space (lower masses, larger mass ratios, or higher spins), we expect that systematic errors in current waveform models may impact gravitational-wave measurements, making more accurate models desirable for future observations.
In this paper we present an extensive analysis of the GW190521 gravitational wave event with the current (fourth) generation of phenomenological waveform models for binary black hole coalescences. GW190521 stands out from other events since only a few wave cycles are observable. This leads to a number of challenges, one being that such short signals are prone to not resolve approximate waveform degeneracies, which may result in multi-modal posterior distributions. The family of waveform models we use includes a new fast time-domain model IMRPhenomTPHM, which allows us extensive tests of different priors and robustness with respect to variations in the waveform model, including the content of spherical harmonic modes. We clarify some issues raised in a recent paper [Nitz&Capano], associated with possible support for a high-mass ratio source, but confirm their finding of a multi-modal posterior distribution, albeit with important differences in the statistical significance of the peaks. In particular, we find that the support for both masses being outside the PISN mass-gap, and the support for an intermediate mass ratio binary are drastically reduced with respect to what Nitz&Capano found. We also provide updated probabilities for associating GW190521 to the potential electromagnetic counterpart from ZTF.
A generic, non-eccentric binary black hole (BBH) system emits gravitational waves (GWs) that are completely described by 7 intrinsic parameters: the black hole spin vectors and the ratio of their masses. Simulating a BBH coalescence by solving Einsteins equations numerically is computationally expensive, requiring days to months of computing resources for a single set of parameter values. Since theoretical predictions of the GWs are often needed for many different source parameters, a fast and accurate model is essential. We present the first surrogate model for GWs from the coalescence of BBHs including all $7$ dimensions of the intrinsic non-eccentric parameter space. The surrogate model, which we call NRSur7dq2, is built from the results of $744$ numerical relativity simulations. NRSur7dq2 covers spin magnitudes up to $0.8$ and mass ratios up to $2$, includes all $ell leq 4$ modes, begins about $20$ orbits before merger, and can be evaluated in $sim~50,mathrm{ms}$. We find the largest NRSur7dq2 errors to be comparable to the largest errors in the numerical relativity simulations, and more than an order of magnitude smaller than the errors of other waveform models. Our model, and more broadly the methods developed here, will enable studies that would otherwise require millions of numerical relativity waveforms, such as parameter inference and tests of general relativity with GW observations.
Gravitational wave astrophysics relies heavily on the use of matched filtering both to detect signals in noisy data from detectors, and to perform parameter estimation on those signals. Matched filtering relies upon prior knowledge of the signals expected to be produced by a range of astrophysical systems, such as binary black holes. These waveform signals can be computed using numerical relativity techniques, where the Einstein field equations are solved numerically, and the signal is extracted from the simulation. Numerical relativity simulations are, however, computationally expensive, leading to the need for a surrogate model which can predict waveform signals in regions of the physical parameter space which have not been probed directly by simulation. We present a method for producing such a surrogate using Gaussian process regression which is trained directly on waveforms generated by numerical relativity. This model returns not just a single interpolated value for the waveform at a new point, but a full posterior probability distribution on the predicted value. This model is therefore an ideal component in a Bayesian analysis framework, through which the uncertainty in the interpolation can be taken into account when performing parameter estimation of signals.
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