{"title":"Adaptive modeling of correlated noise in space-based gravitational wave detectors","authors":"Ya-Nan Li, Yi-Ming Hu and En-Kun Li","doi":"10.1088/1361-6382/ade51a","DOIUrl":null,"url":null,"abstract":"Accurately estimating the statistical properties of noise is important in data analysis for space-based gravitational wave (GW) detectors. Noise in different time-delay interferometry channels correlates with each other. Many studies often assume uncorrelated noise and ignore the off-diagonal elements in the noise covariance matrix. This could lead to some bias in the parameter estimation of GW signals. In this paper, we present a framework for reconstructing the full noise covariance matrix, including frequency-dependent auto- and cross-correlated power spectral densities, without assuming the parametric analytic expressions of the noise model. Our approach combines spline interpolation with trigonometric basis functions to construct a semi-analytical representation of the noise. We then employ trans-dimensional Bayesian inference to fit the correlated noise structure. The resulting software package, NOISAR, successfully recovers both auto- and cross-correlated power spectral features with a relative error of about 10%.","PeriodicalId":10282,"journal":{"name":"Classical and Quantum Gravity","volume":"135 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Classical and Quantum Gravity","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1088/1361-6382/ade51a","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
Accurately estimating the statistical properties of noise is important in data analysis for space-based gravitational wave (GW) detectors. Noise in different time-delay interferometry channels correlates with each other. Many studies often assume uncorrelated noise and ignore the off-diagonal elements in the noise covariance matrix. This could lead to some bias in the parameter estimation of GW signals. In this paper, we present a framework for reconstructing the full noise covariance matrix, including frequency-dependent auto- and cross-correlated power spectral densities, without assuming the parametric analytic expressions of the noise model. Our approach combines spline interpolation with trigonometric basis functions to construct a semi-analytical representation of the noise. We then employ trans-dimensional Bayesian inference to fit the correlated noise structure. The resulting software package, NOISAR, successfully recovers both auto- and cross-correlated power spectral features with a relative error of about 10%.
期刊介绍:
Classical and Quantum Gravity is an established journal for physicists, mathematicians and cosmologists in the fields of gravitation and the theory of spacetime. The journal is now the acknowledged world leader in classical relativity and all areas of quantum gravity.