An efficient iterative method to reduce eccentricity in numerical-relativity simulations of compact binary inspiral

We present a new iterative method to reduce eccentricity in black-hole-binary simulations. Given a good first estimate of low-eccentricity starting momenta, we evolve puncture initial data for ~4 orbits and construct improved initial parameters by comparing the inspiral with post-Newtonian calculations. Our method is the first to be applied directly to the gravitational-wave (GW) signal, rather than the orbital motion. The GW signal is in general less contaminated by gauge effects, which, in moving-puncture simulations, limit orbital-motion-based measurements of the eccentricity to an uncertainty of $\Delta e \sim 0.002$, making it difficult to reduce the eccentricity below this value. Our new method can reach eccentricities below $10^{-3}$ in one or two iteration steps; we find that this is well below the requirements for GW astronomy in the advanced detector era. Our method can be readily adapted to any compact-binary simulation with GW emission, including black-hole-binary simulations that use alternative approaches, and neutron-star-binary simulations. We also comment on the differences in eccentricity estimates based on the strain $h$, and the Newman-Penrose scalar $\Psi_4$.Comment: 24 pages, 25 figures, pdflatex; v2: minor change

Existence of naked singularities in Brans-Dicke theory of gravitation. An analytical and numerical study

Within the framework of the scalar-tensor models of gravitation and by relying on analytical and numerical techniques, we establish the existence of a class of spherically symmetric spacetimes containing a naked singularity. Our result relies on and extends a work by Christodoulou on the existence of naked singularities for the Einstein-scalar field equations. We establish that a key parameter in Christodoulou's construction couples to the Brans-Dicke field and becomes a dynamical variable, which enlarges and modifies the phase space of solutions. We recover analytically many properties first identified by Christodoulou, in particular the loss of regularity (especially at the center), and then investigate numerically the properties of these spacetimes.Comment: 26 pages, PACS numbers: 04.20.Dw, 04.25.dc, 04.50.K

Ready for what lies ahead? -- Gravitational waveform accuracy requirements for future ground based detectors

Future third generation (3G) ground-based GW detectors, such as the Einstein Telescope and Cosmic Explorer, will have unprecedented sensitivities enabling studies of the entire population of stellar mass binary black hole coalescences in the Universe. To infer binary parameters from a GW signal we require accurate models of the gravitational waveform as a function of black hole masses, spins, etc. Such waveform models are built from numerical relativity (NR) simulations and/or semi-analytical expressions in the inspiral. We investigate the limits of the current waveform models and study at what detector sensitivity these models will yield unbiased parameter inference for loud ''golden'' binary black hole systems, what biases we can expect beyond these limits, and what implications such biases will have for GW astrophysics. For 3G detectors we find that the mismatch error for semi-analytical models needs to be reduced by at least \emph{three orders of magnitude} and for NR waveforms by \emph{one order of magnitude}. In addition, we show that for a population of one hundred high mass precessing binary black holes, measurement errors sum up to a sizable population bias, about 10 -- 30 times larger than the sum of 90\% credible intervals for key astrophysical parameters. Furthermore we demonstrate that the residual signal between the GW data recorded by a detector and the best fit template waveform obtained by parameter inference analyses can have significant SNR ratio. This coherent power left in the residual could lead to the observation of erroneous deviations from general relativity. To address these issues and be ready to reap the scientific benefits of 3G GW detectors in the 2030s, waveform models that are significantly more physically complete and accurate need to be developed in the next decade along with major advances in efficiency and accuracy of NR codes

All-sky search for long-duration gravitational wave transients with initial LIGO

We present the results of a search for long-duration gravitational wave transients in two sets of data collected by the LIGO Hanford and LIGO Livingston detectors between November 5, 2005 and September 30, 2007, and July 7, 2009 and October 20, 2010, with a total observational time of 283.0 days and 132.9 days, respectively. The search targets gravitational wave transients of duration 10–500 s in a frequency band of 40–1000 Hz, with minimal assumptions about the signal waveform, polarization, source direction, or time of occurrence. All candidate triggers were consistent with the expected background; as a result we set 90% confidence upper limits on the rate of long-duration gravitational wave transients for different types of gravitational wave signals. For signals from black hole accretion disk instabilities, we set upper limits on the source rate density between 3.4×10−5 and 9.4×10−4  Mpc−3 yr−1 at 90% confidence. These are the first results from an all-sky search for unmodeled long-duration transient gravitational wave

Impact of gravitational radiation higher order modes on single aligned-spin gravitational wave searches for binary black holes

Current template-based gravitational wave searches for compact binary coalescences (CBC) use waveform models that neglect the higher order modes content of the gravitational radiation emitted, considering only the quadrupolar $(\ell,|m|)=(2,2)$ modes. We study the effect of such a neglection for the case of aligned-spin CBC searches for equal-spin (and non-spinning) binary black holes in the context of two versions of Advanced LIGO: the upcoming 2015 version, known as early Advanced LIGO (eaLIGO) and its Zero-Detuned High Energy Power version, that we will refer to as Advanced LIGO (AdvLIGO). In addition, we study the case of a non-spinning search for initial LIGO (iLIGO). We do this via computing the effectualness of the aligned-spin SEOBNRv1 ROM waveform family, which only considers quadrupolar modes, towards hybrid post-Newtonian/Numerical Relativity waveforms which contain higher order modes. We find that for all LIGO versions, losses of more than $10\%$ of events occur for mass ratio $q\geq6$ and $M \geq 100M_\odot$ due to the neglection of higher modes. Moreover, for iLIGO and eaLIGO, losses notably increase up to $(39,23)\%$ respectively for the highest mass $(220M_\odot)$ and mass ratio ($q=8$) studied. For the case of early AdvLIGO, losses of $10\%$ occur for $M>50M_\odot$ and $q\geq6$. Neglection of higher modes leads to observation-averaged systematic parameter biases towards lower spin, total mass and chirp mass. For completeness, we perform a preliminar, non-exhaustive comparison of systematic biases to statistical errors. We find that, for a given SNR, systematic biases dominate over statistical errors at much lower total mass for eaLIGO than for AdvLIGO

Regression methods in waveform modeling: a comparative study

Gravitational-wave astronomy of compact binaries relies on theoretical models of the gravitational-wave signal that is emitted as binaries coalesce. These models do not only need to be accurate, they also have to be fast to evaluate in order to be able to compare millions of signals in near real time with the data of gravitational-wave instruments. A variety of regression and interpolation techniques have been employed to build efficient waveform models, but no study has systematically compared the performance of these regression methods yet. Here we provide such a comparison of various techniques, including polynomial fits, radial basis functions, Gaussian process regression and artificial neural networks, specifically for the case of gravitational waveform modeling. We use all these techniques to regress analytical models of non-precessing and precessing binary black hole waveforms, and compare the accuracy as well as computational speed. We find that most regression methods are reasonably accurate, but efficiency considerations favour in many cases the most simple approach. We conclude that sophisticated regression methods are not necessarily needed in standard gravitational-wave modeling applications, although problems with higher complexity than what is tested here might be more suitable for machine-learning techniques and more sophisticated methods may have side benefits

Incorporating waveform calibration error in gravitational-wave modeling and inference for SEOBNRv4

As gravitational wave (GW) detector networks continue to improve insensitivity, the demand on the accuracy of waveform models which predict the GWsignals from compact binary coalescences is becoming more stringent. At highsignal-to-noise ratios (SNRs) discrepancies between waveform models and thetrue solutions of Einstein's equations can introduce significant systematicbiases in parameter estimation (PE). These biases affect the inferredastrophysical properties, including matter effects, and can also lead toerroneous claims of deviations from general relativity, impacting theinterpretation of astrophysical populations and cosmological parameters. Whileefforts to address these biases have focused on developing more precise models,we explore an alternative strategy to account for uncertainties in waveformmodels, particularly from calibrating an effective-one-body (EOB) model againstnumerical relativity (NR) data. We introduce an efficient method for modelingand marginalizing over waveform uncertainty in the SEOBNRv4 model, whichcaptures the dominant $(2,2)$ mode for non-precessing quasi-circular binaryblack holes (BBHs). Our approach uses Gaussian process regression (GPR) tomodel amplitude and phase deviations in the Fourier domain. This methodmitigates systematic biases in PE and increases posterior variance byincorporating a broader distribution of waveforms, consistent with previousfindings. This study emphasizes the importance of incorporating waveformuncertainties in GW data analysis and presents a novel, practical framework toinclude these uncertainties in Bayesian PE for EOB models, with broadapplicability.<br