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Identifying and Addressing Nonstationary LISA Noise

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arxiv 2004.07515 v2 pith:QQ7Y2YZB submitted 2020-04-16 gr-qc astro-ph.IM

Identifying and Addressing Nonstationary LISA Noise

Matthew C. Edwards , Patricio Maturana-Russel , Renate Meyer , Jonathan Gair , Natalia Korsakova , Nelson Christensen This is my paper
classification gr-qc astro-ph.IM
keywords lisanoisedatanonstationaritieswilladdressingantennaestimation
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We anticipate noise from the Laser Interferometer Space Antenna (LISA) will exhibit nonstationarities throughout the duration of its mission due to factors such as antenna repointing, cyclostationarities from spacecraft motion, and glitches as highlighted by LISA Pathfinder. In this paper, we use a surrogate data approach to test the stationarity of a time series which does not rely on the Gaussianity assumption. The main goal is to identify noise nonstationarities in the future LISA mission. This will be necessary for determining how often the LISA noise power spectral density (PSD) will need to be updated for parameter estimation routines. We conduct a thorough simulation study illustrating the power/size of various versions of the hypothesis tests, and then apply these approaches to differential acceleration measurements from LISA Pathfinder. We also develop a data analysis strategy for addressing nonstationarities in the LISA PSD, where we update the noise PSD over time, while simultaneously conducting parameter estimation, with a focus on planned data gaps.

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Cited by 2 Pith papers

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  1. Signal-to-Noise Ratio Contours for LISA

    gr-qc 2026-07 accept novelty 6.0

    LISA auto-correlation SNR equals the square root of T_obs times the integral of (signal/(noise+signal))^2 and is therefore bounded by sqrt(T_obs(f_max-f_min)).

  2. Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows

    gr-qc 2026-04 unverdicted novelty 6.0

    A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noi...