Time domain maximum likelihood parameter estimation in LISA Pathfinder Data Analysis

TL;DR

This paper introduces a time domain maximum likelihood parameter estimation method for calibrating LISA Pathfinder, demonstrating its effectiveness on simulated and real-like data, crucial for gravitational wave detection accuracy.

Contribution

It presents a novel maximum likelihood estimation technique in the time domain tailored for LISA Pathfinder calibration and validation using simulated and operational data.

Findings

Proven effectiveness on simulated data

Validated robustness against non-standard scenarios

Demonstrated independence from initial guesses and non-Gaussian noise

Abstract

LISA is the upcoming space-based Gravitational Wave telescope. LISA Pathfinder, to be launched in the coming years, will prove and verify the detection principle of the fundamental Doppler link of LISA on a flight hardware identical in design to that of LISA. LISA Pathfinder will collect a picture of all noise disturbances possibly affecting LISA, achieving the unprecedented pureness of geodesic motion necessary for the detection of gravitational waves. The first steps of both missions will crucially depend on a very precise calibration of the key system parameters. Moreover, robust parameters estimation is of fundamental importance in the correct assessment of the residual force noise, an essential part of the data processing for LISA. In this paper we present a maximum likelihood parameter estimation technique in time domain being devised for this calibration and show its proficiency on simulated data and validation through Monte Carlo realizations of independent noise runs. We discuss its robustness to non-standard scenarios possibly arising during the real-life mission, as well as its independence to the initial guess and non-gaussianities. Furthermore, we apply the same technique to data produced in mission-like fashion during operational exercises with a realistic simulator provided by ESA.