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We describe a multivariate classifier for candidate events in a templatedsearch for gravitational-wave (GW) inspiral signals fromneutron-star--black-hole (NS-BH) binaries, in data from ground-based detectorswhere sensitivity is limited by non-Gaussian noise transients. The standardsignal-to-noise ratio (SNR) and chi-squared test for inspiral searches use onlyproperties of a single matched filter at the time of an event; instead, wepropose a classifier using features derived from a bank of inspiral templatesaround the time of each event, and also from a search using approximatesine-Gaussian templates. The classifier thus extracts additional informationfrom strain data to discriminate inspiral signals from noise transients. Weevaluate a Random Forest classifier on a set of single-detector events obtainedfrom realistic simulated advanced LIGO data, using simulated NS-BH signalsadded to the data. The new classifier detects a factor of 1.5 -- 2 more signalsat low false positive rates as compared to the standard 're-weighted SNR'statistic, and does not require the chi-squared test to be computed.Conversely, if only the SNR and chi-squared values of single-detector eventsare available, Random Forest classification performs nearly identically to there-weighted SNR.

A classifier for gravitational-wave inspiral signals in non-ideal single-detector data