Bayesian parameter estimation in the second LISA Pathfinder Mock Data Challenge

TL;DR

This paper demonstrates Bayesian parameter estimation for the LISA Technology Package, achieving optimal error bounds and addressing data combination from multiple experiments to characterize the instrument's noise model.

Contribution

It introduces a Bayesian framework for estimating LTP parameters that reaches the Cramer-Rao bound and proposes methods for combining experimental results.

Findings

Bayesian estimation reaches the Cramer-Rao bound.

Effective combination of multiple experiment results.

Validated noise model parameters for LISA Pathfinder.

Abstract

A main scientific output of the LISA Pathfinder mission is to provide a noise model that can be extended to the future gravitational wave observatory, LISA. The success of the mission depends thus upon a deep understanding of the instrument, especially the ability to correctly determine the parameters of the underlying noise model. In this work we estimate the parameters of a simplified model of the LISA Technology Package (LTP) instrument. We describe the LTP by means of a closed-loop model that is used to generate the data, both injected signals and noise. Then, parameters are estimated using a Bayesian framework and it is shown that this method reaches the optimal attainable error, the Cramer-Rao bound. We also address an important issue for the mission: how to efficiently combine the results of different experiments to obtain a unique set of parameters describing the instrument.