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

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.