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The Ninja data analysis challenge allowed the study of the sensitivity ofdata analysis pipelines to binary black hole numerical relativity waveforms insimulated Gaussian noise at the design level of the LIGO observatory and theVIRGO observatory. We analyzed NINJA data with a pipeline based on the HilbertHuang Transform, utilizing a detection stage and a characterization stage:detection is performed by triggering on excess instantaneous power,characterization is performed by displaying the kernel density enhanced (KD)time-frequency trace of the signal. Using the simulated data based on the twoLIGO detectors, we were able to detect 77 signals out of 126 above SNR 5 incoincidence, with 43 missed events characterized by signal to noise ratio SNRless than 10. Characterization of the detected signals revealed the merger partof the waveform in high time and frequency resolution, free from time-frequencyuncertainty. We estimated the timelag of the signals between the detectorsbased on the optimal overlap of the individual KD time-frequency maps, yieldingestimates accurate within a fraction of a millisecond for half of the events. Acoherent addition of the data sets according to the estimated timelageventually was used in a characterization of the event.

Ninja data analysis with a detection pipeline based on the Hilbert-Huang Transform