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Register a new userBayesian inference for gravitational waves from binary neutron star mergers in third-generation observatories
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Third-generation (3G) gravitational-wave detectors will observe thousands of coalescing neutron star binaries with unprecedented fidelity. Extracting the highest precision science from these signals is expected to be challenging owing to both high signal-to-noise ratios and long-duration signals. We demonstrate that current Bayesian inference paradigms can be extended to the analysis of binary neutron star signals without breaking the computational bank. We construct reduced order models for $sim 90,mathrm{minute}$ long gravitational-wave signals, covering the observing band ($5-2048,mathrm{Hz}$), speeding up inference by a factor of $sim 1.3times 10^4$ compared to the calculation times without reduced order models. The reduced order models incorporate key physics including the effects of tidal deformability, amplitude modulation due to the Earths rotation, and spin-induced orbital precession. We show how reduced order modeling can accelerate inference on data containing multiple, overlapping gravitational-wave signals, and determine the speedup as a function of the number of overlapping signals. Thus, we conclude that Bayesian inference is computationally tractable for the long-lived, overlapping, high signal-to-noise-ratio events present in 3G observatories.
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As current gravitational wave (GW) detectors increase in sensitivity, and particularly as new instruments are being planned, there is the possibility that ground-based GW detectors will observe GWs from highly eccentric neutron star binaries. We present the first detailed study of highly eccentric BNS systems with full (3+1)D numerical relativity simulations using consistent initial conditions, i.e., setups which are in agreement with the Einstein equations and with the equations of general relativistic hydrodynamics in equilibrium. Overall, our simulations cover two different equations of state (EOSs), two different spin configurations, and three to four different initial eccentricities for each pairing of EOS and spin. We extract from the simulated waveforms the frequency of the f-mode oscillations induced during close encounters before the merger of the two stars. The extracted frequency is in good agreement with f-mode oscillations of individual stars for the irrotational cases, which allows an independent measure of the supranuclear equation of state not accessible for binaries on quasi-circular orbits. The energy stored in these f-mode oscillations can be as large as $10^{-3}M_odot sim 10^{51}$ erg, even with a soft EOS. In order to estimate the stored energy, we also examine the effects of mode mixing due to the stars offset from the origin on the f-mode contribution to the GW signal. While in general (eccentric) neutron star mergers produce bright electromagnetic counterparts, we find that the luminosity decreases when the eccentricity becomes too large, due to a decrease of the ejecta mass. Finally, the use of consistent initial configurations also allows us to produce high-quality waveforms for different eccentricities which can be used as a testbed for waveform model development of highly eccentric binary neutron star systems.
We continue our study of the binary neutron star parameter space by investigating the effect of the spin orientation on the dynamics, gravitational wave emission, and mass ejection during the binary neutron star coalescence. We simulate seven different configurations using multiple resolutions to allow a reasonable error assessment. Due to the particular choice of the setups, five configurations show precession effects, from which two show a precession (wobbling) of the orbital plane, while three show a bobbing motion, i.e., the orbital angular momentum does not precess, while the orbital plane moves along the orbital angular momentum axis. Considering the ejection of mass, we find that precessing systems can have an anisotropic mass ejection, which could lead to a final remnant kick of $sim 40 rm km/s$ for the studied systems. Furthermore, for the chosen configurations, antialigned spins lead to larger mass ejecta than aligned spins, so that brighter electromagnetic counterparts could be expected for these configurations. Finally, we compare our simulations with the precessing, tidal waveform approximant IMRPhenomPv2_NRTidalv2 and find good agreement between the approximant and our numerical relativity waveforms with phase differences below 1.2 rad accumulated over the last $sim$ 16 gravitational wave cycles.
We construct new, multivariate empirical relations for measuring neutron star radii and tidal deformabilities from the dominant gravitational wave frequency in the post-merger phase of binary neutron star mergers. The relations determine neutron star radii and tidal deformabilities for specific neutron star masses with consistent accuracy and depend only on two observables: the post-merger peak frequency $f_{rm peak}$ and the chirp mass $M_{rm chirp}$. The former could be measured with good accuracy from gravitational waves emitted in the post-merger phase using next-generation detectors, whereas the latter is already obtained with good accuracy from the inspiral phase with present-day detectors. Our main data set consists of a gravitational wave catalogue obtained with CFC/SPH simulations. We also extract the $f_{rm peak}$ frequency from the publicly available CoRe data set, obtained through grid-based GRHD simulations and find good agreement between the extracted frequencies of the two data sets. As a result, we can construct empirical relations for the combined data sets. Furthermore, we investigate empirical relations for two secondary peaks, $f_{2-0}$ and $f_{rm spiral}$, and show that these relations are distinct in the whole parameter space, in agreement with a previously introduced spectral classification scheme. Finally, we show that the spectral classification scheme can be reproduced using machine-learning techniques.
We show how gravitational-wave observations with advanced detectors of tens to several tens of neutron-star binaries can measure the neutron-star radius with an accuracy of several to a few percent, for mass and spatial distributions that are realistic, and with none of the sources located within 100 Mpc. We achieve such an accuracy by combining measurements of the total mass from the inspiral phase with those of the compactness from the postmerger oscillation frequencies. For estimating the measurement errors of these frequencies we utilize analytical fits to postmerger numerical-relativity waveforms in the time domain, obtained here for the first time, for four nuclear-physics equations of state and a couple of values for the mass. We further exploit quasi-universal relations to derive errors in compactness from those frequencies. Measuring the average radius to well within 10% is possible for a sample of 100 binaries distributed uniformly in volume between 100 and 300 Mpc, so long as the equation of state is not too soft or the binaries are not too heavy.
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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