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We present bajes, a parallel and lightweight framework for Bayesian inference of multimessenger transients. bajes is a python modular package with minimal dependencies on external libraries adaptable to the majority of the Bayesian models and to various sampling methods. We describe the general workflow and the parameter estimation pipeline for compact-binary-coalescence gravitational-wave transients. The latter is validated against injections of binary black hole and binary neutron star waveforms, including confidence interval tests that demonstrate the inference is well calibrated. Binary neutron star postmerger injections are also studied using a network of five detectors made of LIGO, Virgo, KAGRA, and Einstein Telescope. Postmerger signals will be detectable for sources at ≲80 Mpc, with Einstein Telescope contributing over 90% of the total signal-to-noise ratio. As a full scale application, we reanalyze the gravitational-wave transients catalog-1 black hole transients using the effective-one-body teobresums approximant and reproduce selected results with other approximants. bajes inferences are consistent with previous results; the direct comparison of bajes and bilby analyses of GW150914 shows a maximum Jensen-Shannon divergence of 5.2×10-4. GW170817 is reanalyzed using taylorf2 with 5.5PN point mass and 7.5PN tides, teobresumspa, and imrphenompv2_nrtidal with different cutoff frequencies of 1024 and 2048 Hz. We find that the former choice minimizes systematics on the reduced tidal parameter, while a larger amount of tidal information is gained with the latter choice. bajes can perform these analyses in about 1 day using 128 CPUs. [ABSTRACT FROM AUTHOR]

Gravitational-wave signals from binary neutron-star coalescences carry information about the star's equation of state in their tidal signatures. A major issue in the inference of the tidal parameters (or, directly, of the equation of state) is the systematic error introduced by the waveform approximants. We use a bottom-up approach based on gauge-invariant phase analysis and the Fisher information matrix to investigate waveform systematics and identify biases in parameter estimation. A mock analysis of 15 different binaries indicates that systematics in current waveform models dominate over statistical errors at signal-to-noise ratio (SNR) ≳80 for LIGO and Virgo at design sensitivity. This implies biases in the inference of the reduced tidal parameter that are larger than the statistical 90% credible intervals. For example, while the neutron-star radius could be constrained at the ∼5% level at SNR 80, systematics can be at the ∼10% level. We apply our approach to GW170817 (SNR∼30) and confirm that no significant systematic effects are present. Using frequencies below ≲1 kHz for the analysis, we estimate a neutron-star radius of 12.5+1.1-1.8 km. The latter is consistent with an electromagnetic-informed prior and the recent NICER measurement. Exploring SNR≳100 in view of third-generation detectors, we find that all the current waveform models lead to differences of at least 1σ in the inference of the reduced tidal parameter. We conclude that current waveform models, including those from numerical relativity, are insufficient to infer the equation of state in the loudest (and potentially most informative) events that will be observed by advanced and third-generation detectors. [ABSTRACT FROM AUTHOR]

We apply machine learning methods to build a time-domain model for gravitational waveforms from binary black hole mergers, called mlgw. The dimensionality of the problem is handled by representing the waveform's amplitude and phase using a principal component analysis. We train mlgw on about O(10³) teobresums and seobnrv4 effective-one-body waveforms with mass ratios q∈[1,20] and aligned dimensionless spins s∈[-0.80,0.95]. The resulting models are faithful to the training sets at the ∼10-3 level (averaged on the parameter space). The speed up for a single waveform generation is a factor of 10-50 (depending on the binary mass and initial frequency) for teobresums and approximately an order of magnitude more for seobnrv4. Furthermore, mlgw provides a closed form expression for the waveform and its gradient with respect to the orbital parameters; such an information might be useful for future improvements in gravitational wave (GW) data analysis. As demonstration of the capabilities of mlgw to perform a full parameter estimation, we reanalyze the public data from the first GW transient catalog (GWTC-1). We find broadly consistent results with previous analyses at a fraction of the cost, although the analysis with spin-aligned waveforms gives systematic larger values of the effective spins with respect to previous analyses with precessing waveforms. Since the generation time does not depend on the length of the signal, our model is particularly suitable for the analysis of the long signals that are expected to be detected by third-generation detectors. Future applications include the analysis of waveform systematics and model selection in parameter estimation. [ABSTRACT FROM AUTHOR]

The remnant star of a neutron star merger is an anticipated loud source of kilohertz gravitational waves that conveys unique information on the equation of state of hot matter at extreme densities. Observations of such signals are hampered by the photon shot noise of ground-based interferometers and pose a challenge for gravitational-wave astronomy. We develop an analytical time-domain waveform model for postmerger signals informed by numerical relativity simulations. The model completes effective-one-body waveforms for quasicircular nonspinning binaries in the kilohertz regime. We show that a template-based analysis can detect postmerger signals with a minimal signal-to-noise ratio (SNR) of 8.5, corresponding to GW170817-like events for third-generation interferometers. Using Bayesian model selection and the complete inspiral-merger-postmerger waveform model it is possible to infer whether the merger outcome is a prompt collapse to a black hole or a remnant star. In the latter case, the radius of the maximum mass (most compact) nonrotating neutron star can be determined to kilometer precision. We demonstrate the feasibility of inferring the stiffness of the equation of state at extreme densities using the quasiuniversal relations deduced from numerical-relativity simulations. [ABSTRACT FROM AUTHOR]

The use of effective-one-body (EOB) waveforms for black hole binaries analysis in gravitational-wave astronomy requires faithful models and fast generation times. A key aspect to achieve faithfulness is the inclusion of numerical-relativity (NR) informed next-to-quasicircular corrections (NQC), dependent on the radial momentum, to the waveform and radiation reaction. A robust method to speed up the waveform generation is the postadiabatic iteration to approximate the solution of the EOB Hamiltonian equations. In this work, we assess the performances of a fast NQC prescription in combination to the postadiabatic method for generating multipolar gravitational waves. The outlined approach allows a consistent treatment of NQC in both the waveform and the radiation-reaction, does not require iterative procedures to achieve high faithfulness, and can be efficiently employed for parameter estimation. Comparing to 611 NR simulations, for total mass 10  M⊙≤M≤200  M⊙ and using the Advanced LIGO noise, the model has EOB/NR unfaithfulness well below 0.01, with 78.5% of the cases below 0.001. We apply the model to the parameter estimation of GW150914 exploring the impact of the new NQC and of the higher modes up to ℓ=m=8. [ABSTRACT FROM AUTHOR]

The gravitational wave GW170817 is associated with the inspiral phase of a binary neutron star coalescence event. The LIGO-Virgo detectors' sensitivity at high frequencies was not sufficient to detect the signal corresponding to the merger and postmerger phases. Hence, the question whether the merger outcome was a prompt black-hole formation or not must be answered using either the premerger gravitational-wave signal or electromagnetic counterparts. In this work we present two methods to infer the probability of prompt black-hole formation, using the analysis of the inspiral gravitational-wave signal. Both methods combine the posterior distribution from the gravitational-wave data analysis with numerical-relativity results. One method relies on the use of phenomenological models for the equation of state and on the estimate of the collapse threshold mass. The other is based on the estimate of the tidal polarizability parameter ˜Λ that is correlated in an equation-of-state agnostic way with the prompt black-hole formation. We analyze GW170817 data and find that the two methods consistently predict a probability of ∼50%-70% for prompt black-hole formation, which however may significantly decrease below 10% if the maximum mass constraint from PSR J0348+0432 or PSR J0740+6620 is imposed. [ABSTRACT FROM AUTHOR]