TDI-infinity: time-delay interferometry without delays

Abstract

The space-based gravitational-wave observatory LISA relies on a form of synthetic interferometry (time-delay interferometry, or TDI) where the otherwise overwhelming laser phase noise is canceled by linear combinations of appropriately delayed phase measurements. These observables grow in length and complexity as the realistic features of the LISA orbits are taken into account. In this paper we outline an implicit formulation of TDI where we write the LISA likelihood directly in terms of the basic phase measurements, and we marginalize over the laser phase noises in the limit of infinite laser-noise variance. Equivalently, we rely on TDI observables that are defined numerically (rather than algebraically) from a discrete-filter representation of the laser propagation delays. Our method generalizes to any time dependence of the armlengths; it simplifies the modeling of gravitational-wave signals; and it allows a straightforward treatment of data gaps and missing measurements.

7 pages, 3 figures

References in corpus (7)

• Laser Interferometer Space Antenna
• New advances in the Gaussian-process approach to pulsar-timing data analysis
• LISACode : A scientific simulator of LISA
• Sensitivity and parameter-estimation precision for alternate LISA configurations
• Gravitational-wave parameter estimation with gaps in LISA: a Bayesian data augmentation method
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• PyROQ: a Python-based Reduced Order Quadrature Building Code for Fast Gravitational Wave Inference
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• Charactering instrumental noises and stochastic gravitational wave signals from combined time-delay interferometry
• Bayesian Characterisation of Circumbinary Exoplanets with LISA
• Sparse data inpainting for the recovery of Galactic-binary gravitational wave signals from gapped data
• The Varied Avatars of Time-delay Interferometry
• Time delay interferometry with minimal null frequencies
• Observable-based reformulation of time-delay interferometry
• Model-independent time-delay interferometry based on principal component analysis
• Accelerating the time-domain LISA response model with central finite differences and hybridization techniques
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