Adaptive Modeling of Correlated Noise in Space-Based Gravitational Wave Detectors
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911008535085056 |
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| author | Li, Ya-Nan Hu, Yi-Ming Li, En-Kun |
| author_facet | Li, Ya-Nan Hu, Yi-Ming Li, En-Kun |
| contents | Accurately estimating the statistical properties of noise is important in data analysis for space-based gravitational wave 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 gravitational wave 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, $\texttt{NOISAR}$, successfully recovers both auto- and cross-correlated power spectral features with a relative error of about $10\%$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12983 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Modeling of Correlated Noise in Space-Based Gravitational Wave Detectors Li, Ya-Nan Hu, Yi-Ming Li, En-Kun Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology Accurately estimating the statistical properties of noise is important in data analysis for space-based gravitational wave 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 gravitational wave 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, $\texttt{NOISAR}$, successfully recovers both auto- and cross-correlated power spectral features with a relative error of about $10\%$. |
| title | Adaptive Modeling of Correlated Noise in Space-Based Gravitational Wave Detectors |
| topic | Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2504.12983 |