Measuring precession in asymmetric compact binaries

Pratten, G.; Schmidt, P.; Buscicchio, Riccardo; Thomas, Linden

doi:10.1103/physrevresearch.2.043096

Cited by 46 publications

(42 citation statements)

References 171 publications

(255 reference statements)

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“…To simulate the observed data we use the waveform model IMRPhenomPv2 [29] and assume a zero noise realization. The component masses are set to m 1 = 20M , m 2 = 5M , as unequal masses are known to maximize the effect of precession, and hence deviation from elliptical polarization [30,31]. Besides the system's mass ratio, the binary inclination and the amount of in-plane spin also affect the degree to which a signal is precession-modulated.…”

Section: A Signal Polarizationmentioning

confidence: 99%

BayesWave analysis pipeline in the era of gravitational wave observations

et al. 2021

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We describe updates and improvements to the BayesWave gravitational wave transient analysis pipeline, and provide examples of how the algorithm is used to analyze data from ground-based gravitational wave detectors. BayesWave models gravitational wave signals in a morphology-independent manner through a sum of frame functions, such as Morlet-Gabor wavelets or chirplets. BayesWave models the instrument noise using a combination of a parametrized Gaussian noise component and non-stationary and non-Gaussian noise transients. Both the signal model and noise model employ trans-dimensional sampling, with the complexity of the model adapting to the requirements of the data. The flexibility of the algorithm makes it suitable for a variety of analyses, including reconstructing generic unmodeled signals; cross checks against modeled analyses for compact binaries; as well as separating coherent signals from incoherent instrumental noise transients (glitches). The BayesWave model has been extended to account for gravitational wave signals with generic polarization content and the simultaneous presence of signals and glitches in the data. We describe updates in the BayesWave prior distributions, sampling proposals, and burn-in stage that provide significantly improved sampling efficiency. We present standard review checks indicating the robustness and convergence of the BayesWave trans-dimensional sampler.

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Section: A Signal Polarizationmentioning

confidence: 99%

BayesWave analysis pipeline in the era of gravitational wave observations

et al. 2021

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“…In this section we consider the effect of changing the orientation of our standard configuration, which allows us to quantify how it will manifest in realistic LIGO-Virgo signals. A related study has looked at the effect at higher mass ratios [52].…”

Section: Inclinationmentioning

confidence: 99%

“…Bayes factors have thus far been the gold standard for identifying evidence for precession within the GW community and have been used extensively in previous works, see e.g., Ref. [52].…”

Section: Relating ρ P Posteriors To Bayes Factorsmentioning

confidence: 99%

“…Here, we investigate the relationship between the Bayes factor in favor of precession and the precession SNR ρ p . Both of these quantities have been used together in recent works when assessing the evidence for observable precession [4,5,52] For a subset of the runs described in Sec. IV C, we reran the analysis using the aligned-spin waveform model IMRPhenomD.…”

Section: Relating ρ P Posteriors To Bayes Factorsmentioning

confidence: 99%

“…Beyond these general expectations, the quantitative behavior of parameter measurements in the presence of precession has not been studied in great detail for typical LIGO-Virgo-KAGRA observations. The measurability of precession for high mass ratio LIGO-Virgo-KAGRA observations like GW190814 has been investigated in recent work [52].…”

Section: Introductionmentioning

confidence: 99%

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Identifying when precession can be measured in gravitational waveforms

et al. 2021

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In binary-black-hole systems where the black-hole spins are misaligned with the orbital angular momentum, precession effects leave characteristic modulations in the emitted gravitational waveform. Here, we investigate where in the parameter space we will be able to accurately identify precession, for likely observations over coming LIGO-Virgo-KAGRA observing runs. Despite the large number of parameters that characterize a precessing binary, we perform a large scale systematic study to identify the impact of each source parameter on the measurement of precession. We simulate a fiducial binary at moderate mass-ratio, signal-to-noise ratio (SNR), and spins, such that precession will be clearly identifiable, then successively vary each parameter while holding the remaining parameters fixed. As expected, evidence for precession increases with signal-to noise-ratio (SNR), higher in-plane spins, more unequal component masses, and higher inclination, but our study provides a quantitative illustration of each of these effects, and informs our intuition on parameter dependencies that have not yet been studied in detail, for example, the effect of varying the relative strength of the two polarizations, the total mass, and the aligned-spin components. We also measure the "precession SNR" ρ p , to quantify the signal power associated with precession. By comparing ρ p with both Bayes factors and the recovered posterior distributions, we find it is a reliable metric for measurability that accurately predicts when the detected signal contains evidence for precession.

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Signatures of hierarchical mergers in black hole spin and mass distribution

Tagawa

Haiman

Bartos

et al. 2021

Monthly Notices of the Royal Astronomical Society

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Recent gravitational wave (GW) observations by LIGO/Virgo show evidence for hierarchical mergers, where the merging BHs are the remnants of previous BH merger events. These events may carry important clues about the astrophysical host environments of the GW sources. In this paper, we present the distributions of the effective spin parameter (χeff), the precession spin parameter (χp), and the chirp mass (mchirp) expected in hierarchical mergers. Under a wide range of assumptions, hierarchical mergers produce (i) a monotonic increase of the average of the typical total spin for merging binaries, which we characterize with ${\bar{\chi }}_\mathrm{typ}\equiv \overline{(\chi _\mathrm{eff}^2+\chi _\mathrm{p}^2)^{1/2}}$, up to roughly the maximum mchirp among first-generation (1g) BHs, and (ii) a plateau at ${\bar{\chi }}_\mathrm{typ}\sim 0.6$ at higher mchirp. We suggest that the maximum mass and typical spin magnitudes for 1g BHs can be estimated from ${\bar{\chi }}_\mathrm{typ}$ as a function of mchirp. The GW data observed in LIGO/Virgo O1–O3a prefers an increase in ${\bar{\chi }}_\mathrm{typ}$ at low mchirp, which is consistent with the growth of the BH spin magnitude by hierarchical mergers, at ∼2σ confidence. A Bayesian analysis using the χeff, χp, and mchirp distributions suggests that 1g BHs have the maximum mass of ∼15–30 M⊙ if the majority of mergers are of high-generation BHs (not among 1g-1g BHs), which is consistent with mergers in active galactic nucleus disks and/or nuclear star clusters, while if mergers mainly originate from globular clusters, 1g BHs are favored to have non-zero spin magnitudes of ∼0.3. We also forecast that signatures for hierarchical mergers in the ${\bar{\chi }}_\mathrm{typ}$ distribution can be confidently recovered once the number of GW events increases to ≳ O(100).

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Measuring precession in asymmetric compact binaries

Cited by 46 publications

References 171 publications

BayesWave analysis pipeline in the era of gravitational wave observations

BayesWave analysis pipeline in the era of gravitational wave observations

Identifying when precession can be measured in gravitational waveforms

Signatures of hierarchical mergers in black hole spin and mass distribution

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