Leveraging Time-Dependent Instrumental Noise for LISA SGWB Analysis

TL;DR

This paper shows how time-dependent instrumental noise variations in LISA can be exploited to enhance sensitivity to stochastic gravitational wave backgrounds, using optimal data segmentation and simulation-based inference methods.

Contribution

It introduces a novel approach to utilize non-stationary noise fluctuations in LISA data analysis for improved SGWB detection sensitivity.

Findings

Noise fluctuations can be leveraged for better sensitivity.

Optimal segmentation improves information gain.

Simulation-based inference is effective for this purpose.

Abstract

Variations in the instrumental noise of the Laser Interferometer Space Antenna (LISA) over time are expected as a result of e.g. scheduled satellite operations or unscheduled glitches. We demonstrate that these fluctuations can be leveraged to improve the sensitivity to stochastic gravitational wave backgrounds (SGWBs) compared to the stationary noise scenario. This requires optimal use of data segments with downward noise fluctuations, and thus a data analysis pipeline capable of analysing and combining shorter time segments of mission data. We propose that simulation based inference is well suited for this challenge. In an approximate, but state-of-the-art, modeling setup, we show by comparison with Fisher Information Matrix estimates that the optimal information gain can be achieved in practice.