Noisy Pipelines I: An end-to-end pipeline to perform multivariate noise inference for LISA
Abstract
Instrumental data from the Laser Interferometer Space Antenna (LISA) must be post-processed prior to any global-fit based analysis. For example, it is critical to filter, downsample, segment and window the data in order to perform inference of both the signal and noise parameters in the frequency domain. Each step alters the noise process, requiring a correction to the noise covariance matrix that describes the underlying data stream. We present lisa−noise, a JAX-based noise inference pipeline that returns a spectral estimate of the noise process subject to post-processing effects. We fit splines to the underlying secondary noises and fit the full multivariate 3 × 3 covariance matrix of the Michelson channels. We assume unequal and non-constant arm-lengths throughout. The drift of the zeros of the transfer functions, a non-stationary effect, is captured by averaging the transfer functions over a number of epochs within each segment. On 30 days of conditioned simulated data, a likelihood evaluation costs 3ms on a current laptop for a 7 day segment. A gradient-based fit from a tight prior recovers the injected spectra in a fraction of a second. A multi-start search over large priors ± 10 in log10 centred on the simulated truth obtains a noise estimate consistent with the data, verified by Bayesian techniques. The fitted covariance whitens the conditioned data in every band and through every zero of the transfer functions, in a form that global-fit pipelines can use directly.
Related
- Noisy pipelines II: Non-parametric Noise Inference for LISA
- The LSST Science Pipelines Software: Optical Survey Pipeline Reduction and Analysis Environment
- SGN: A python framework for stream-processing pipelines
- The best of all worlds: gravitational-wave transient detection with multiple pipelines
- Efficient searches of small signals across two-channel noisy data: a challenge in Big Data observations
BibTeX
@misc{burke2026noisy,
title = {{Noisy Pipelines I: An end-to-end pipeline to perform multivariate noise inference for LISA}},
author = {Ollie Burke and Joseph Bayley and Charlie Hoy and Laura Nuttall},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.28726},
eprint = {2610.28726},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.28726},
note = {CC0},
}
RIS
TY - UNPB
TI - Noisy Pipelines I: An end-to-end pipeline to perform multivariate noise inference for LISA
AU - Ollie Burke
AU - Joseph Bayley
AU - Charlie Hoy
AU - Laura Nuttall
PY - 2026
DA - 2026/10/20
PB - arXiv
DO - 10.48550/arxiv.2610.28726
UR - https://arxiv.org/abs/2610.28726
AB - Instrumental data from the Laser Interferometer Space Antenna (LISA) must be post-processed prior to any global-fit based analysis. For example, it is critical to filter, downsample, segment and window the data in order to perform inference of both the signal and noise parameters in the frequency domain. Each step alters the noise process, requiring a correction to the noise covariance matrix that describes the underlying data stream. We present lisa−noise, a JAX-based noise inference pipeline that returns a spectral estimate of the noise process subject to post-processing effects. We fit splines to the underlying secondary noises and fit the full multivariate 3 × 3 covariance matrix of the Michelson channels. We assume unequal and non-constant arm-lengths throughout. The drift of the zeros of the transfer functions, a non-stationary effect, is captured by averaging the transfer functions over a number of epochs within each segment. On 30 days of conditioned simulated data, a likelihood evaluation costs 3ms on a current laptop for a 7 day segment. A gradient-based fit from a tight prior recovers the injected spectra in a fraction of a second. A multi-start search over large priors ± 10 in log_10 centred on the simulated truth obtains a noise estimate consistent with the data, verified by Bayesian techniques. The fitted covariance whitens the conditioned data in every band and through every zero of the transfer functions, in a form that global-fit pipelines can use directly.
ER -