Bayesian inference for binary neutron star inspirals using a Hamiltonian Monte Carlo Algorithm

Understand

The coalescence of binary neutron stars are one of the main sources of gravitational waves for ground-based gravitational wave detectors.

• As Bayesian inference for binary neutron stars is computationally expensive, more efficient and faster converging algorithms are always needed.
• In this work, we conduct a feasibility study using a Hamiltonian Monte Carlo algorithm (HMC).
• The HMC is a sampling algorithm that takes advantage of gradient information from the geometry of the parameter space to efficiently sample from the posterior distribution, allowing the algorithm to avoid the random-walk behaviour commonly associated with stochastic samplers.