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

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

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

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

Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference

We combine amortized neural posterior estimation with importance sampling for fast and accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian posterior using neural networks, and then attach importance weights based on the underlying likelihood and prior. This provides (1) a corrected posterior free from network inaccuracies, (2) a performance diagnostic (the sample efficiency) for assessing the proposal and identifying failure cases, and (3) an unbiased estimate of the Bayesian evidence. By establishing this independent verification and correction mechanism we address some of the most frequent criticisms against deep learning for scientific inference. We carry out a large study analyzing 42 binary black hole mergers observed by LIGO and Virgo with the SEOBNRv4PHM and IMRPhenomXPHM waveform models. This shows a median sample efficiency of $\approx 10\%$ (two orders-of-magnitude better than standard samplers) as well as a ten-fold reduction in the statistical uncertainty in the log evidence. Given these advantages, we expect a significant impact on gravitational-wave inference, and for this approach to serve as a paradigm for harnessing deep learning methods in scientific applications

Adapting to noise distribution shifts in flow-based gravitational-wave inference

Deep learning techniques for gravitational-wave parameter estimation haveemerged as a fast alternative to standard samplers \unicode{x2013} producingresults of comparable accuracy. These approaches (e.g., DINGO) enable amortizedinference by training a normalizing flow to represent the Bayesian posteriorconditional on observed data. By conditioning also on the noise power spectraldensity (PSD) they can even account for changing detector characteristics.However, training such networks requires knowing in advance the distribution ofPSDs expected to be observed, and therefore can only take place once all datato be analyzed have been gathered. Here, we develop a probabilistic model toforecast future PSDs, greatly increasing the temporal scope of DINGO networks.Using PSDs from the second LIGO-Virgo observing run (O2) \unicode{x2013} plusjust a single PSD from the beginning of the third (O3) \unicode{x2013} weshow that we can train a DINGO network to perform accurate inference throughoutO3 (on 37 real events). We therefore expect this approach to be a key componentto enable the use of deep learning techniques for low-latency analyses ofgravitational waves.<br