Results: Choosing a generator for streaming Gaussian noise in gravitational-wave mock data
Year: 2026
Type: dataset
Abstract: Measured results behind the tables, the data figures and the numbers quoted from them in the paper Choosing a generator for streaming Gaussian noise in gravitational-wave mock data: accuracy, cost and reproducibility, together with the noise spectra the results were computed for. The paper compares generators of long, streaming Gaussian noise records with a target power spectral density: the crossfade of independent frequency-domain realizations used by gwmock-noise and LALSimulation, PyCBC's reproducible filtered noise, overlap-save FIR filtering, and autoregressive and ARMA models. It reports their exact covariance error, throughput, retained state and the cost of regenerating a segment bit for bit, across Advanced LIGO, Advanced Virgo, KAGRA, Cosmic Explorer and Einstein Telescope sensitivity curves. Files. results.tar.gz unpacks into results/ (191 JSON files) and psd/ (16 spectrum tables). README.md documents the layout, the fields common to every file and the measured quantities of each kind of result. results/exact/: the covariance error of every generator configuration, per spectrum, band-edge treatment and sampling rate. results/costs/: throughput, memory and state of every generator. results/implementation/: generated noise compared with its exact spectrum. results/reproducibility.json: bit-exact regeneration of a segment, its stored state and rebuild cost. results/verification.json: the measured crossfade covariance against its closed form. results/edge-leakage/: predicted and measured coherence near the band edges. psd/: the one-sided power spectral density of each spectrum. Provenance. Each result file records the run configuration, the random seeds of a sampled run or the rule that derives them (the exact errors are computed, not sampled, and have none), the gwmock-noise commit that generated the noise, the revision of the analysis code (with no uncommitted changes in any file), library versions, the hardware, and the run's wall time, CPU time and peak memory. Run labels were replaced by descriptive ones for this release, and non-finite numbers are written as null so that every file is strict JSON; no other measured value, configuration, seed or commit differs from the run's output. Reproducing the paper. The scripts that fetch this record and reproduce the paper's tables and figures, including its two analytic schematic figures, which need no data, are at github.com/isaac-cf-wong/noise-stitching-statistics-data.
Author: Wong, Isaac C. F.
Institution: KU Leuven
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7224994478