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In the flourishing field of gravitational-wave astronomy, accuratelyinferring binary black hole merger formation channels is paramount. TheBayesian 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 channelsabsent in known models can bias branching fraction estimates. In this researchnote, we introduce a test to detect missing channels in such analyses. Ourfindings 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.

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