Multivariate Classification with Random Forests for Gravitational Wave Searches of Black Hole Binary Coalescence

Abstract

Searches for gravitational waves produced by coalescing black hole binaries with total masses $\gtrsim 25$M${}_{\odot }$ use matched filtering with templates of short duration. Non-Gaussian noise bursts in gravitational wave detector data can mimic short signals and limit the sensitivity of these searches. Previous searches have relied on empirically designed statistics incorporating signal-to-noise ratio and signal-based vetoes to separate gravitational wave candidates from noise candidates. We report on sensitivity improvements achieved using a multivariate candidate ranking statistic derived from a supervised machine learning algorithm. We apply the random forest of bagged decision trees technique to two separate searches in the high mass $\left(\gtrsim 25{\mathrm{M}}_{\odot }\right)$ parameter space. For a search which is sensitive to gravitational waves from the inspiral, merger, and ringdown (IMR) of binary black holes with total mass between $25$M${}_{\odot }$ and $100$M${}_{\odot }$, we find sensitive volume improvements as high as $70_{\pm 13}-109_{\pm 11}$\% when compared to the previously used ranking statistic. For a ringdown-only search which is sensitive to gravitational waves from the resultant perturbed intermediate mass black hole with mass roughly between $10$M${}_{\odot }$ and $600$M${}_{\odot }$, we find sensitive volume improvements as high as $61_{\pm 4}-241_{\pm 12}$\% when compared to the previously used ranking statistic. We also report how sensitivity improvements can differ depending on mass regime, mass ratio, and available data quality information. Finally, we describe the techniques used to tune and train the random forest classifier that can be generalized to its use in other searches for gravitational waves.

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