Speaker
Description
The Laser Interferometer Space Antenna (LISA) will observe gravitational waves produced by several massive black hole binary (MBHB) mergers per year. While the likelihood can be written in closed form under idealised stationary, Gaussian noise assumptions, including realistic effects — instrumental glitches, gaps in the data, and non-stationary noise — make it intractable or computationally prohibitive. This motivates the development of Simulation-based inference (SBI) pipeline, which requires only forward simulations of the data and can thus in principle include arbitrary complex physics.
We present proof-of-concept results using truncated marginal neural ratio estimation (TMNRE), a sequential SBI method that iteratively truncates the prior to the region supporting non-negligible posterior mass. One of the challenges is the enormous shrinkage of prior volume under the posterior, a factor ~$10^{-24}$, which makes ordinary MCMC and sequential SBI difficult.
We demonstrate inference of the parameters of a single MBHB from frequency-domain LISA data with a customised TMNRE algorithm, accommodating the periodicity of the angular parameters and preserving multimodal support during truncation, and validate the method against the MCMC posterior of the tractable case (obtained by artificially reducing the prior volume around the fiducial parameters). For a mock observation with chirp mass $10^{5.25} M_{\odot}$ and SNR 2200, we correctly recover the MCMC posterior over the 11-dimensional parameter space, in about 30 hours of training on a NVIDIA A100 GPU.