Time domain maximum likelihood parameter estimation in LISA Pathfinder data analysis

Abstract

LISA is the upcoming space-based gravitational-wave detector. LISA Pathfinder, to be launched in the coming years, will be the in-flight test of the LISA arm, with a hardware (control scheme, sensors, and actuators) identical in design to LISA. LISA Pathfinder will collect a picture of all noise disturbances possibly affecting LISA, achieving the unprecedented pureness of geodesic motion of test masses 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 has a fundamental importance in the correct assessment of the residual acceleration noise between the test masses, an essential part of the data preprocessing for LISA. In this paper, we present a maximum likelihood parameter estimation technique in time domain employed for system identification, 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 nonstandard scenarios possibly arising during the real mission. Furthermore, we apply the same technique to data produced in missionlike fashion during operational exercises with a realistic simulator provided by European Space Agency. The result of the investigation is that parameter estimation is mandatory to avoid systematic errors in the estimated differential acceleration noise.