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.
Related
- Noisy Pipelines I: An end-to-end pipeline to perform multivariate noise inference for LISA
- A space of inference spaces in the space sciences - parametric Bayesian inference in astronomy, cosmology and particle physics
- Gravitational-Wave Inference For Noise PSD Jumps Across Data Gaps
- Mind the second gap: The impact of gaps on noise and signal parameter inference
- A parametrized test of general relativity for inspiralling eccentric binaries in LISA
BibTeX
@misc{hoy2026noisy,
title = {{Noisy pipelines II: Non-parametric Noise Inference for LISA}},
author = {Charlie Hoy and Joseph Bayley and Ollie Burke and Laura K. Nuttall},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.03911},
eprint = {2611.03911},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.03911},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Noisy pipelines II: Non-parametric Noise Inference for LISA
AU - Charlie Hoy
AU - Joseph Bayley
AU - Ollie Burke
AU - Laura K. Nuttall
PY - 2026
DA - 2026/11/02
PB - arXiv
DO - 10.48550/arxiv.2611.03911
UR - https://arxiv.org/abs/2611.03911
AB - 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.
ER -