Bayesian statistics for the calibration of the LISA Pathfinder experiment

M Armano ,P Binétruy ,L Ferraioli ,E Mitchell ,J Huesler ,William J Weber ,C García Marirrodriga ,M Freschi ,M Caleno ,J Slutsky ,S Waschke ,B Johlander ,D Vetrugno ,C Trenkel ,E Fitzsimons ,D Mance ,K Danzmann ,O Jennrich ,I Mateos ,I Lloro ,S Vitale ,P W Mcnamara ,M Perreur-Lloyd ,S Paczkowski ,H Rozemeijer ,H Ward ,D Bortoluzzi ,A Schleicher ,B Kaune ,D Hollington ,C J Killow ,Shiming Wen ,J Reiche ,M Hueller ,Natalia Korsakova ,Nikolaos Karnesis ,U Ragnit ,I Harrison ,M Nofrarias ,T Ziegler ,Philippe Jetzer ,Paolo Sarra ,R Dolesi ,N Brandt ,A Bursi ,P Prat ,H Audley ,R Giusteri ,G Auger ,A Cesarini ,M Born ,Carlos F Sopuerta ,R Gerndt ,Gerhard Heinzel ,James Thorpe ,Domenico Giardini ,R Maarschalkerweerd ,Hai-Bo Tu ,V Ferroni ,D I Robertson ,José Antonio Pérez ,M Hewitson ,S Madden ,H Inchauspé ,F Martín-Porqueras ,C Grimani ,N Dunbar ,D Texier ,Vicente Martín ,A Moroni ,José F G Mendes ,E Plagnol ,Peter Wass ,T J Sumner ,C Zanoni ,G Russano ,P Pivato ,A Wittchen ,Jose Ramos-Castro ,Antonella Cavalleri ,I Diepholz ,F Gibert ,L Gesa ,M Cruise ,Gudrun Wanner ,D Wealthy ,Peter Zweifel

doi:10.1088/1742-6596/610/1/012027

Abstract

The main goal of LISA Pathfinder (LPF) mission is to estimate the acceleration noise models of the overall LISA Technology Package (LTP) experiment on-board. This will be of crucial importance for the future space-based Gravitational-Wave (GW) detectors, like eLISA. Here, we present the Bayesian analysis framework to process the planned system identification experiments designed for that purpose. In particular, we focus on the analysis strategies to predict the accuracy of the parameters that describe the system in all degrees of freedom. The data sets were generated during the latest operational simulations organised by the data analysis team and this work is part of the LTPDA Matlab toolbox. A post-publication change was made to this article on 26 Jun 2020 to add an author.

Highlights

The LISA Pathfinder mission [1] is a joint mission of the European Space Agency (ESA) and the National Aeronautics and Space Administration (NASA)
This work is fully integrated in the standardised LTPDA toolbox [3], and is part of the pipeline analysis to be performed during mission operations
We have developed a Bayesian framework to be applied for the analysis of the LISA Pathfinder (LPF) mission planned system identification experiments

Summary

Introduction

The LISA Pathfinder mission [1] is a joint mission of the European Space Agency (ESA) and the National Aeronautics and Space Administration (NASA). Where the S∆1/a2 term, is the spectral density of the differential acceleration between the TMs. In order to reach the level of Eq (1), the instrument must be fully characterised during the mission, and the various unknown dynamical parameters need to be estimated. Where θ the parameter set to be estimated given the data-set y, π(y|θ) the likelihood function, p(θ) the prior Probability Density Functions (PDFs), and π(y) is the so-called evidence of the model.

Results

Conclusion