Ameliorating transient noise bursts in gravitational-wave searches for intermediate-mass black holes

The direct observation of intermediate-mass black holes (IMBH) populations would not only strengthen the possible evolutionary link between stellar and supermassive black holes, but unveil the details of the pair-instability mechanism and elucidate their influence in galaxy formation. Conclusive observation of IMBHs remained elusive until the detection of gravitational-wave (GW) signal GW190521, which lies with high confidence in the mass gap predicted by the pair-instability mechanism. Despite falling in the sensitivity band of current GW detectors, IMBH searches are challenging due to their similarity to transient bursts of detector noise, known as glitches. In this proof-of-concept work, we combine a matched-filter algorithm with a machine learning (ML) method to differentiate IMBH signals from nontransient burst noise, known as glitches. In particular, we build a multilayer perceptron network to perform a multiclass classification of the output triggers of matched filter. In this way we are able to distinguish simulated GW IMBH signals from different classes of glitches that occurred during the third observing run (O3) in single detector data. We train, validate, and test our model on O3a data, reaching a true positive rate of over 90% for simulated IMBH signals. To test the generalization ability over the evolutionary observing run, we test on the unseen data of O3b, which yields a true positive rate of over 70%. We also combine data from multiple detectors to search for simulated IMBH signals in real detector noise, providing a significance measure for the output of our ML method.