Orateur
Description
Massive black hole binaries (MBHBs) are one of the main targets of the LISA mission, with numerous science objectives ranging from astrophysics to comsology. These signals will reach very high signal-to-noise ratios (SNRs), from hundreds up to thousands, opening the possibility of high-precision scientific measurements, such as the search for deviations from General Relativity. However, in order to enable the full potential of the instrument, waveform models must be accurate enough to keep systematic errors subdominant with respect to statistical errors. Inaccurate waveform models could cause biases in the parameter estimation, as well as leave residuals in the data stream after signal subtraction, affecting the LISA global fit. Focusing on spin-aligned circular systems, we present a first exploration of these biases for example MBHB signals, using a full simulated Bayesian inference, and investigate the relation between large SNRs and accuracty requirements.