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Numerical Relativity has been of fundamental importance for studying compactbinary coalescence dynamics, waveform modelling, and eventually forgravitational waves observations. As the sensitivity of the detector networkimproves, more precise template modelling will be necessary to guarantee a moreaccurate estimation of astrophysical parameters. To help improving the accuracyof numerical relativity catalogs, we developed a deep learning model capable ofdetecting anomalous waveforms. We analyzed 1341 binary black hole simulationsfrom the SXS catalog with various mass-ratios and spins, considering waveformdominant and higher modes. In the set of waveform analyzed, we found andcategorised seven types of anomalies appearing close to the merger phase.

Deep learning waveform anomaly detector for numerical relativity catalogs