Identifying and mitigating machine-learning biases for the gravitational-wave detection problem

Matched filtering is a long-standing technique for the optimal detection of known signals in stationary Gaussian noise. However, it has known departures from optimality when operating on unknown signals in real noise and suffers from computational inefficiencies in its pursuit of near-optimality. A compelling alternative that has emerged in recent years to address this problem is deep learning. Although it has shown significant promise when applied to the search for gravitational-waves in detector noise, we demonstrate the existence of a multitude of learning biases that hinder generalization and lead to significant loss in detection sensitivity. Our work identifies the sources of a set of 11 interconnected biases present in the supervised learning of the gravitational-wave detection problem and contributes mitigation tactics and training strategies to concurrently address them. In light of the identified biases, we demonstrate that existing detection sensitivity metrics are not reliable for machine-learning pipelines and discuss the trustworthiness of previous results. We use gravitational-wave domain knowledge to build a bespoke machine-learning-based binary black hole search pipeline called Sage that addresses these biases. Via the injection study presented in the Machine-Learning Gravitational-Wave Search Challenge, we show that Sage detects ≈ 11.2 % more signals than the benchmark y analysis at a false-alarm rate of one per month in O3a noise. Moreover, we also show that it can detect ≈ 48.29 % more signals than the previous best-performing machine-learning pipeline on the same dataset. We empirically prove that Sage can (i) effectively handle out-of-distribution noise power spectral densities, (ii) strongly reject non-Gaussian transient noise artifacts, and (iii) achieve higher detection sensitivities using less data than network architectures of a similar size. By studying machine-learning biases and conducting empirical investigations to understand the reasons for performance improvement or degradation, we aim to address the need for interpretability of machine-learning methods for gravitational-wave science. All code and implementations are available.