Inference with finite time series II: the window strikes back

Colm Talbot; Sylvia Biscoveanu; Aaron Zimmerman; Tomasz Baka; Will M. Farr; Jacob Golomb; Charlie Hoy; Andrew Lundgren; Jacopo Tissino; Michael J. Williams; John Veitch; Aditya Vijaykumar

arXiv · 2508.11091

Inference with finite time series II: the window strikes back

Colm Talbot·Sylvia Biscoveanu·Aaron Zimmerman·Tomasz Baka·Will M. Farr·Jacob Golomb·Charlie Hoy·Andrew Lundgren·Jacopo Tissino·Michael J. Williams·John Veitch·Aditya Vijaykumar

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Abstract

Smooth window functions are often applied to strain data when inferring the parameters describing the astrophysical sources of gravitational-wave transients. Within the LIGO-Virgo-KAGRA collaboration, it is conventional to include a term to account for power loss due to this window in the likelihood function. We show that the inclusion of this factor leads to biased inference. The simplest solution to this, omitting the factor, leads to unbiased posteriors and Bayes factor estimates provided the window does not suppress the signal for signal-to-noise ratios $\lesssim O(100)$, but unreliable estimates of the absolute likelihood. Instead, we propose a multi-stage method that yields consistent estimates for the absolute likelihood in addition to unbiased posterior distributions and Bayes factors for signal-to-noise ratios $\lesssim O(1000)$. Additionally, we demonstrate that the commonly held wisdom that using rectangular windows necessarily leads to biased inference is incorrect.

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Colm Talbot, Sylvia Biscoveanu, Aaron Zimmerman, Tomasz Baka, Will M. Farr, Jacob Golomb, Charlie Hoy, Andrew Lundgren, Jacopo Tissino, Michael J. Williams, John Veitch, Aditya Vijaykumar. 2025-08-14. Inference with finite time series II: the window strikes back. https://arxiv.org/abs/2508.11091

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