Search

We introduce a novel method to generate a bank of gravitational-waveform templates of binary Black Hole (BBH) coalescences for matched-filter searches in LIGO, Virgo and Kagra data. Unlike the standard approach, our method relies on a numerical metric approximation of the distance between templates, which makes the template placement orders-of-magnitude faster than with existing techniques. Our method applies to a variety of different manifolds of signals and is particularly suitable for covering high-dimensional spaces, such as those associated with precessing and/or eccentric waveforms. We compare our method with the state-of-the-art stochastic placement code and find that our code slightly overcovers the space, while achieving similar efficiency in recovering signals. To demonstrate the capabilities of our code, we generate a bank for precessing Black Holes, a bank for intermediate-mass Black Holes with higher-order modes, and an eccentric bank, and show that they cover the space in a satisfactory way. Our publicly released code mbank will enable searches of high-dimensional regions of BBH signal space, hitherto unfeasible due to the prohibitive cost of bank generation.

We present a surrogate model of \texttt{SEOBNRv4PHM}, a fully precessing time-domain effective-one-body waveform model including subdominant modes. We follow an approach similar to that used to build recent numerical relativity surrogate models. Our surrogate is 5000M in duration, covers mass-ratios up to 1:20 and dimensionless spin magnitudes up to 0.8. Validating the surrogate against an independent test set we find that the bulk of the surrogate errors is less than $\sim 1\%$ in mismatch, which is similar to the modelling error of \texttt{SEOBNRv4PHM} itself. At high total mass a few percent of configurations can exceed this threshold if they are highly precessing and they exceed a mass-ratio of 1:4. This surrogate is nearly two orders of magnitude faster than the underlying time-domain \texttt{SEOBNRv4PHM} model and can be evaluated in $\sim 50$ ms. Bayesian inference analyses with \texttt{SEOBNRv4PHM} are typically very computationally demanding and can take from weeks to months to complete. The two order of magnitude speedup attained by our surrogate model enables practical parameter estimation analyses with this waveform family. This is \emph{crucial} because Bayesian inference allows us to recover the masses and spins of binary black hole mergers given a model of the emitted gravitational waveform along with a description of the noise., 14 pages, 11 figures

We describe a general mathematical framework for $��^2$ discriminators in the context of the compact binary coalescence search. We show that with any $��^2$ is associated a vector bundle over the signal manifold, that is, the manifold traced out by the signal waveforms in the function space of data segments. The $��^2$ is then defined as the square of the $L_2$ norm of the data vector projected onto a finite dimensional subspace (the fibre) of the Hilbert space of data trains and orthogonal to the signal waveform - any such fibre leads to a $��^2$ discriminator and the full vector bundle comprising the subspaces and the base manifold constitute the $��^2$ discriminator. We show that the $��^2$ discriminators used so far in the CBC searches correspond to different fiber structures constituting different vector bundles on the same base manifold, namely, the parameter space. The general formulation indicates procedures to formulate new $��^2$s which could be more effective in discriminating against commonly occurring glitches in the data. It also shows that no $��^2$ with a reasonable degree of freedom is foolproof. It could also shed light on understanding why the traditional $��^2$ works so well. As an example, we propose a family of ambiguity $��^2$ discriminators that is an alternative to the traditional one. Any such ambiguity $��^2$ makes use of the filtered output of the template bank, thus adding negligible cost to the overall search. We test the performance of ambiguity $��^2$ on simulated data using spinless TaylorF2 waveforms. We show that the ambiguity $��^2$ essentially gives a clean separation between glitches and signals. Finally, we investigate the effects of mismatch between signal and templates on the $��^2$ and also further indicate how the ambiguity $��^2$ can be generalized to detector networks for coherent observations., 21 pages, 5 figure, abstract is shortened to comply with the arXiv's 1920 characters limitation, v2: accepted for publication in PRD