Mass$\unicode{x2013}$spin Re-Parameterization for Rapid Parameter Estimation of Inspiral Gravitational-Wave Signals

Abstract

Estimating the source parameters of gravitational waves from compact binary coalescence(CBC) is a key analysis task in gravitational-wave astronomy. To deal with the increasing detection rate of CBC signals, optimizing the parameter estimation analysis is crucial. The analysis typically employs a stochastic sampling technique such as Markov Chain Monte Carlo(MCMC), where the source parameter space is explored and regions of high Bayesian posterior probability density are found. One of the bottlenecks slowing down the analysis is the non-trivial correlation between masses and spins of colliding objects, which makes the exploration of mass$\unicode{x2013}$spin space extremely inefficient. We introduce a new set of mass$\unicode{x2013}$spin sampling parameters which makes the posterior distribution to be simple in the new parameter space, regardless of the true values of the parameters. The new parameter combinations are obtained as the principal components of the Fisher matrix for the restricted 1.5 post-Newtonian waveform. Our re-parameterization improves the efficiency of MCMC by a factor of $\sim 10$ for binary neutron star with narrow-spin prior ($|\vec{\chi }|<0.05$) and $\sim 100$ with broad-spin prior ($|\vec{\chi }|<0.99$), under the assumption that the binary has spins aligned with its orbital angular momentum.

17 pages, 12 figures

References in corpus (6)

• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
• Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
• GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
• Detecting binary neutron star systems with spin in advanced gravitational-wave detectors
• Early warning of precessing compact binary merger with third-generation gravitational-wave detectors

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• Accelerated parameter estimation of supermassive black hole binaries in LISA using a meshfree approximation
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• labrador: A domain-optimized machine-learning tool for gravitational wave inference