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arXiv · 2310.10736

Detecting the Undetected: Overcoming Biases in Gravitational-Wave Population Studies

Ryan Raikman·Simone Bavera·Tassos Fragos
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Abstract

In the flourishing field of gravitational-wave astronomy, accurately inferring binary black hole merger formation channels is paramount. The Bayesian hierarchical model selection analysis offers a promising methodology (see, e.g., One Channel to Rule Them All, Zevin et al. 2021). However, recently, Cheng et al. (2023) highlighted a critical caveat: observed channels absent in known models can bias branching fraction estimates. In this research note, we introduce a test to detect missing channels in such analyses. Our findings show a commendable success rate in identifying these elusive channels. Yet, in scenarios where missing channels closely overlap with recognized ones, discerning the difference remains challenging.

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Ryan Raikman, Simone Bavera, Tassos Fragos. 2023-10-16. Detecting the Undetected: Overcoming Biases in Gravitational-Wave Population Studies. https://arxiv.org/abs/2310.10736

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