Simulation-based Inference for Gravitational Waves from Binary Neutron Stars: Application of Summary Data from Heterodyning

TL;DR

This paper introduces a new likelihood-based data compression method for gravitational wave signals from binary neutron stars, enabling faster neural posterior estimation with validated accuracy.

Contribution

It presents a novel compression strategy using likelihood-oriented summary statistics that improves efficiency for neural inference in gravitational wave parameter estimation.

Findings

The compression reduces data size to about 1000 points, lowering training and storage costs.

The neural posterior estimator achieves well-calibrated results across parameters.

Potential for rapid and efficient BNS gravitational wave inference is demonstrated.

Abstract

Gravitational-wave parameter estimation for binary neutron star (BNS) systems poses severe computational challenges due to the extended signal duration, which can reach several minutes in current detectors. Neural posterior estimation (NPE), a simulation-based inference approach, offers dramatic speedups but requires effective dimensionality reduction of the high-dimensional input data. We present a novel compression strategy based on likelihood-oriented summary statistics derived from the relative binning formalism of Zackay et al. (2018), which compresses raw frequency-domain data into the summary data. The summary data is based on a polynomial approximation of the waveform ratio using frequency banding grounded in post-Newtonian approximation, and directly evaluated with only $O(1000)$ sample points of the waveform. As a result, both the training and storage cost become more efficient than previously reported networks for BNS inference. We train a set of NPE networks on these summary statistics and validate a network against traditional nested sampling over 1024 BNS injections. The network produces well-calibrated posteriors across all source parameters we consider, with Jensen-Shannon divergences (JSD) consistent with numerical noise for most parameters. Although we find that the median JSD for the most inconsistent parameter exceeds $10^{-2}$ bits with current configurations, our results show potential for rapid parameter estimation of the BNS signal.