# Gravitational-wave parameter estimation with gaps in LISA: a Bayesian data augmentation method

## TL;DR

This paper introduces a Bayesian data augmentation method to improve gravitational-wave parameter estimation from gapped LISA data, addressing noise leakage and computational challenges caused by measurement interruptions.

## Contribution

The paper presents a novel Bayesian data augmentation approach for analyzing gapped LISA data, enhancing parameter estimation accuracy and efficiency.

## Key findings

- Method effectively handles data gaps in gravitational-wave analysis.
- Improves parameter estimation accuracy compared to complete data assumptions.
- Reduces noise leakage and computational complexity.

## Abstract

By listening to gravity in the low frequency band, between 0.1 mHz and 1 Hz, the future space-based gravitational-wave observatory LISA will be able to detect tens of thousands of astrophysical sources from cosmic dawn to the present. The detection and characterization of all resolvable sources is a challenge in itself, but LISA data analysis will be further complicated by interruptions occurring in the interferometric measurements. These interruptions will be due to various causes occurring at various rates, such as laser frequency switches, high-gain antenna re-pointing, orbit corrections, or even unplanned random events. Extracting long-lasting gravitational-wave signals from gapped data raises problems such as noise leakage and increased computational complexity. We address these issues by using Bayesian data augmentation, a method that reintroduces the missing data as auxiliary variables in the sampling of the posterior distribution of astrophysical parameters. This provides a statistically consistent way to handle gaps while improving the sampling efficiency and mitigating leakage effects. We apply the method to the estimation of galactic binaries parameters with different gap patterns, and we compare the results to the case of complete data.