Robust Bayesian inference with gapped LISA data using all-in-one TDI-$\infty$

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Summary

The Laser Interferometer Space Antenna (LISA) mission aims to detect gravitational waves in the millihertz frequency band. However, LISA data analysis faces challenges due to laser frequency noise and data gaps. A new method, TDI-∞, has been developed to address these issues by reframing parameter inference and marginalizing over laser phase noises.

Highlights

• LISA mission aims to detect gravitational waves in the millihertz frequency band.
• Classical TDI mitigates laser frequency noise but is sensitive to data gaps.
• TDI-∞ is a new method that addresses laser noise and data gaps by reframing parameter inference.
• TDI-∞ marginalizes over laser phase noises under the assumption of infinite noise variance.
• The method is integrated into a Bayesian framework for parameter estimation.
• TDI-∞ demonstrates superior performance in scenarios involving data gaps.
• The study's results highlight the potential of TDI-∞ to enhance LISA's scientific capabilities.

Key Insights

• TDI-∞ offers a robust solution to the challenges posed by laser frequency noise and data gaps in LISA data analysis. By reframing parameter inference and marginalizing over laser phase noises, TDI-∞ demonstrates improved performance in scenarios involving data gaps.
• The integration of TDI-∞ into a Bayesian framework enables the estimation of astrophysical parameters from LISA data, even in the presence of data gaps.
• TDI-∞ has the potential to enhance LISA's scientific capabilities by providing a more robust and accurate method for data analysis.
• The study's results highlight the importance of developing new methods to address the challenges of LISA data analysis.
• TDI-∞ can handle measurement interruptions and discontinuities, removing the need to explicitly address discontinuities during gravitational-wave template matching.
• The method's ability to preserve signal integrity more effectively than classical TDI makes it particularly interesting for low-latency applications.
• The study's findings have implications for the development of more robust data analysis pipelines for LISA and other gravitational wave detectors.

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Citation

Houba, N., Bayle, J.-B., & Vallisneri, M. (2024). Robust Bayesian inference with gapped LISA data using all-in-one TDI-$\infty$ (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2412.20793