相关论文: Accelerating multimodal gravitational waveforms fr…
Binary black hole (BBH) mergers detected via gravitational waves are addressing key open questions in astrophysics, cosmology, and fundamental physics. Our scientific conclusions rely on extracting accurate source parameters, for which we…
We introduce a highly-parallelizable architecture for estimating parameters of compact binary coalescence using gravitational-wave data and waveform models. Using a spherical harmonic mode decomposition, the waveform is expressed as a sum…
We present IMRPhenomXODE, a new phenomenological frequency-domain waveform approximant for gravitational wave (GW) signals from precessing binary black holes (BBHs) with generic spin configurations. We build upon the success of…
Astrophysical black holes could be nearly extremal (that is, rotating nearly as fast as possible); therefore, nearly extremal black holes could be among the binaries that current and future gravitational-wave observatories will detect.…
The coalescence of binary black holes (BBHs) provides a unique arena to test general relativity (GR) in the dynamical, strong-field regime. To this end, we present pSEOBNRv5PHM, a parametrized, multipolar, spin-precessing waveform model for…
The availability of accurate numerical waveforms is an important requirement for the creation and calibration of reliable waveform models for gravitational wave astrophysics. For black hole-neutron star binaries, very few accurate waveforms…
We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers whose waveforms include the higher order gravitational wave modes $(l, |m|)=\{(2, 2), (2, 1), (3, 3), (3, 2), (4,…
The first direct detection of neutron-star-black-hole binaries will likely be made with gravitational-wave observatories. Advanced LIGO and Advanced Virgo will be able to observe neutron-star-black-hole mergers at a maximum distance of…
Deep learning can be used to drastically decrease the processing time of parameter estimation for coalescing binaries of compact objects including black holes and neutron stars detected in gravitational waves (GWs). As a first step, we…
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…
Previous analytic and numerical calculations suggest that, at each instant, the emission from a precessing black hole binary closely resembles the emission from a nonprecessing analog. In this paper we quantitatively explore the validity…
Gravitational waves emitted by neutron star black hole mergers encode key properties of neutron stars - such as their size, maximum mass and spins - and black holes. However, the presence of matter and the high mass ratio makes generating…
Coalescing binary black holes are among the primary science targets for second generation ground-based gravitational wave (GW) detectors. Reliable GW models are central to detection of such systems and subsequent parameter estimation. This…
As gravitational-wave (GW) observations of binary black holes are becoming a precision tool for physics and astronomy, several subdominant effects in the GW signals need to be accurately modeled. Previous studies have shown that neglecting…
We present a fast Bayesian inference framework to address the growing computational cost of gravitational-wave parameter estimation. The increased cost is driven by improved broadband detector sensitivity, particularly at low frequencies…
We construct hybrid binary black holes merger waveforms using analytical model waveforms for the early inspiral phase and numerical relativity waveforms for late inspiral to merger and post merger phases. To hybridize analytical and…
We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 hours. Once fully trained, we optimized…
The catalog of gravitational-wave events is growing, and so are our hopes of constraining the underlying astrophysics of stellar-mass black-hole mergers by inferring the distributions of, e.g., masses and spins. While conventional analyses…
Gravitational waves from compact binaries measured by the LIGO and Virgo detectors are routinely analyzed using Markov Chain Monte Carlo sampling algorithms. Because the evaluation of the likelihood function requires evaluating millions of…
We propose a machine learning-based approach for parameter estimation of Massive Black Hole Binaries (MBHBs), leveraging normalizing flows to approximate the likelihood function. By training these flows on simulated data, we can generate…