Noisy pipelines II: Non-parametric Noise Inference for LISA

Abstract

The Laser Interferometer Space Antenna (LISA) is expected to observe gravitational-wave signals from massive black hole binaries, extreme mass ratio inspirals, and more. Despite the significant scientific potential, accurately extracting these signals from the data will only be possible if LISA's instrumental noise is understood: poor noise modelling will result in lost signals, biased parameter estimates, and incorrect astrophysical insights. Modelling LISA's instrumental noise is more challenging than its ground-based counterpart since the data is highly correlated, meaning that the noise covariance matrix can no longer be assumed diagonal in frequency and in the different data streams. In this work, we present an initial approach to perform non-parametric noise inference for LISA that requires no prior knowledge of the LISA observatory. By fitting the full noise covariance matrix with a combination of splines and inverse Lorentzians, we show that our model can estimate the power and cross spectral densities for LISA from 7 days of simulated noise-only data. We find that the median estimate from our non-parametric model whitens the data according to the expected unit normal distribution. We envision that our non-parametric model can be used as part of the global fit, as well as in combination with modelled approaches to identify, quantify, and constrain the frequency dependence of unknown noise artifacts.