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Register a new userTime domain phenomenological model of gravitational wave subdominant harmonics for quasi-circular non-precessing binary black hole coalescences
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H\\'ector Estell\\'es
Publication date
2020
fields
Physics
and research's language is
English
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In this work we present an extension of the time domain phenomenological model IMRPhenomT for gravitational wave signals from binary black hole coalescences to include subdominant harmonics, specifically the $(l=2, m=pm 1)$, $(l=3, m=pm 3)$, $(l=4, m=pm 4)$ and $(l=5, m=pm 5)$ spherical harmonics. We also improve our model for the dominant $(l=2, m=pm 2)$ mode and discuss mode mixing for the $(l=3, m=pm 2)$ mode. The model is calibrated to numerical relativity solutions of the full Einstein equations up to mass ratio 18, and to numerical solutions of the Teukolsky equations for higher mass ratios. This work complements the latest generation of traditional frequency domain phenomenological models (IMRPhenomX), and provides new avenues to develop computationally efficient models for gravitational wave signals from generic compact binaries.
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In this work we present IMRPhenomTP, a time domain phenomenological model for the dominant $l=2$, $m=|2|$ modes of coalescing black hole binary systems and its extension to describe general precessing systems within the twisting up approximation. The underlying non-precessing model is calibrated to the new release of Numerical Relativity simulations of the SXS Collaboration and its accuracy is comparable to the state-of-the-art non-precessing dominant mode models as IMRPhenomX and SEOBNRv4. The precessing extension allows for flexibility choosing the Euler angles of the time-dependent rotation between the co-precessing and the inertial reference systems, including the single spin NNLO and the double spin MSA PN descriptions present in other models, numerical integration of the orbit averaged spin evolution equations, different choices for the evolution of the orbital angular momentum norm and a simple approximation to the ringdown behaviour.
When two black holes merge, a tremendous amount of energy is released in the form of gravitational radiation in a short span of time, making such events among the most luminous phenomenon in the universe. Models that predict the peak luminosity of black hole mergers are of interest to the gravitational wave community, with potential applications in tests of general relativity. We present a surrogate model for the peak luminosity that is directly trained on numerical relativity simulations of precessing binary black holes. Using Gaussian process regression, we interpolate the peak luminosity in the 7-dimensional parameter space of precessing binaries with mass ratios $qleq4$, and spin magnitudes $chi_1,chi_2leq0.8$. We demonstrate that our errors in estimating the peak luminosity are lower than those of existing fitting formulae by about an order of magnitude. In addition, we construct a model for the peak luminosity of aligned-spin binaries with mass ratios $qleq8$, and spin magnitudes $|chi_{1z}|,|chi_{2z}|leq0.8$. We apply our precessing model to infer the peak luminosity of the GW event GW190521, and find the results to be consistent with previous predictions.
Searches for gravitational-wave transients from binary black hole coalescences typically rely on one of two approaches: matched filtering with templates and morphology-independent excess power searches. Multiple algorithmic implementations in the analysis of data from the first generation of ground-based gravitational wave interferometers have used different strategies for the suppression of non-Gaussian noise transients, and targeted different regions of the binary black hole parameter space. In this paper we compare the sensitivity of three such algorithms: matched filtering with full coalescence templates, matched filtering with ringdown templates and a morphology-independent excess power search. The comparison is performed at a fixed false alarm rate and relies on Monte-carlo simulations of binary black hole coalescences for spinning, non-precessing systems with total mass 25-350 solar mass, which covers the parameter space of stellar mass and intermediate mass black hole binaries. We find that in the mass range of 25 -100 solar mass the sensitive distance of the search, marginalized over source parameters, is best with matched filtering to full waveform templates, to within 10 percent at a false alarm rate of 3 events per year. In the mass range of 100-350 solar mass, the same comparison favors the morphology-independent excess power search to within 20 percent. The dependence on mass and spin is also explored.
The spin distribution of binary black hole mergers contains key information concerning the formation channels of these objects, and the astrophysical environments where they form, evolve and coalesce. To quantify the suitability of deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers, we introduce a modified version of WaveNet trained with a novel optimization scheme that incorporates general relativistic constraints of the spin properties of astrophysical black holes. The neural network model is trained, validated and tested with 1.5 million $ell=|m|=2$ waveforms generated within the regime of validity of NRHybSur3dq8, i.e., mass-ratios $qleq8$ and individual black hole spins $ | s^z_{{1,,2}} | leq 0.8$. Using this neural network model, we quantify how accurately we can infer the astrophysical parameters of black hole mergers in the absence of noise. We do this by computing the overlap between waveforms in the testing data set and the corresponding signals whose mass-ratio and individual spins are predicted by our neural network. We find that the convergence of high performance computing and physics-inspired optimization algorithms enable an accurate reconstruction of the mass-ratio and individual spins of binary black hole mergers across the parameter space under consideration. This is a significant step towards an informed utilization of physics-inspired deep learning models to reconstruct the spin distribution of binary black hole mergers in realistic detection scenarios.
Over the past year, a handful of new gravitational wave models have been developed to include multiple harmonic modes thereby enabling for the first time fully Bayesian inference studies including higher modes to be performed. Using one recently-developed numerical relativity surrogate model, NRHybSur3dq8, we investigate the importance of higher modes on parameter inference of coalescing massive binary black holes. We focus on examples relevant to the current three-detector network of observatories, with a detector-frame mass set to $120 M_odot$ and with signal amplitude values that are consistent with plausible candidates for the next few observing runs. We show that for such systems the higher mode content will be important for interpreting coalescing binary black holes, reducing systematic bias, and computing properties of the remnant object. Even for comparable-mass binaries and at low signal amplitude, the omission of higher modes can influence posterior probability distributions. We discuss the impact of our results on source population inference and self-consistency tests of general relativity. Our work can be used to better understand asymmetric binary black hole merger events, such as GW190412. Higher modes are critical for such systems, and their omission usually produces substantial parameter biases.
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