# Robust parameter estimation within minutes on gravitational wave signals from binary neutron star inspirals

**Authors:** Thibeau Wouters, Peter T. H. Pang, Tim Dietrich, Chris Van Den Broeck  
**arXiv:** [2404.11397](https://arxiv.org/abs/2404.11397)  
**Date:** 2024-04-17  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2404.11397v1/

## Key points

- Study focuses on robust parameter estimation for gravitational wave signals from binary neutron star inspirals
- Signals contain valuable information about nuclear matter above saturation density
- Authors extend the Jim framework to include tidal effects in the analysis of gravitational wave signals from binary neutron star mergers
- Jim framework combines relative binning and jax features into a normalizing flow-enhanced sampler for efficient Bayesian parameter estimation
- Efficient parameter estimation pipeline like Jim is highlighted for various scientific applications and environmentally friendly implementation in gravitational wave parameter estimation

## Summary

The study by Wouters et al. focuses on robust parameter estimation for gravitational wave signals from binary neutron star inspirals. These signals contain valuable information about nuclear matter above saturation density. However, extracting this information and conducting parameter estimation is typically a computationally challenging and expensive task. To address this issue, the authors build upon the work of Wong et al., who introduced the Jim framework for parameter estimation in binary black hole mergers. In their study, Wouters et al. extend the Jim framework to include tidal effects in the analysis of gravitational wave signals from binary neutron star mergers. The Jim framework combines relative binning and jax features such as hardware acceleration and automatic differentiation into a normalizing flow-enhanced sampler. This approach allows for full Bayesian parameter estimation of gravitational waves from binary neutron star mergers within a few tens of minutes. This includes training the normalizing flow and computing reference parameters for relative binning. For example, using the TaylorF2 waveform for GW170817, Jim can complete the analysis in 26 minutes, while it takes around 21 minutes for both waveforms of GW190425. The authors highlight the importance of an efficient parameter estimation pipeline like Jim for various scientific applications and emphasize its environmentally friendly implementation in gravitational wave parameter estimation. They also provide access to all code used in their study through a GitHub repository and offer to share posterior samples upon request. In addition to demonstrating its effectiveness in accurately estimating parameters from gravitational wave signals through comparison with pBilby results, Wouters et al. 's study showcases five keywords: , , , , and . These keywords represent the key components and contributions of their work, making it easier for readers to understand and identify the main focus of the study.