Improving significance of binary black hole mergers in Advanced LIGO data using deep learning: Confirmation of GW151216

We present a novel machine learning (ML) based strategy to search for binary black hole (BBH) mergers in data from ground-based gravitational wave (GW) observatories. This is the first ML-based search that not only recovers all the compact binary coalescences (CBCs) in the first GW transients catalog (GWTC-1), but also makes a clean detection of GW151216, which was not significant enough to be included in the catalog. Moreover, we achieve this by only adding a new coincident ranking statistic (MLStat) to a standard analysis that was used for GWTC-1. In CBC searches, reducing contamination by terrestrial and instrumental transients, which create a loud noise background by triggering numerous false alarms, is crucial to improving the sensitivity for detecting true events. The sheer volume of data and a large number of expected detections also prompts the use of ML techniques. We perform transfer learning to train ``InceptionV3,'' a pretrained deep neural network, along with curriculum learning to distinguish GW signals from noisy events by analyzing their continuous wavelet transform (CWT) maps. MLStat incorporates information from this ML classifier into the coincident search likelihood used by the standard pycbc search. This leads to at least an order of magnitude improvement in the inverse false-alarm-rate (IFAR) for the previously ``low significance'' events GW151012, GW170729, and GW151216. We also perform the parameter estimation of GW151216 using seobnrv4hm_rom. We carry out an injection study to show that MLStat brings substantial improvement to the detection sensitivity of Advanced LIGO for all compact binary coalescences. The average improvement in the sensitive volume is $\ensuremath{\sim}10%$ for low chirp masses ($0.8--5\text{ }\text{ }{\mathrm{M}}_{\ensuremath{\bigodot}}$) and $\ensuremath{\sim}30%$ for higher masses ($5--50\text{ }\text{ }{\mathrm{M}}_{\ensuremath{\bigodot}}$). Performance in the lower masses may become even better if the training set for the ML classifier, currently restricted to black hole binaries with component masses in the range $2--98\text{ }\text{ }{\mathrm{M}}_{\ensuremath{\bigodot}}$ only, is expanded to include binaries with neutron stars. Considering the impressive ability of the statistic to distinguish signals from glitches, the list of marginal events from MLStat could be quite reliable for astrophysical population studies and further follow-up. This work demonstrates the immense potential and readiness of MLStat for finding new sources in current data and the possibility of its adaptation in similar searches.