Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using YOLO One-Shot Object Detection

Abstract

We demonstrate the application of the YOLOv5 model, a general purpose convolution-based single-shot object detection model, in the task of detecting binary neutron star (BNS) coalescence events from gravitational-wave data of current generation interferometer detectors. We also present a thorough explanation of the synthetic data generation and preparation tasks based on approximant waveform models used for the model training, validation and testing steps. Using this approach, we achieve mean average precision (mAP [0.50] ) values of 0.945 for a single class validation dataset and as high as 0.978 for test datasets. Moreover, the trained model is successful in identifying the GW170817 event in the LIGO H1 detector data. The identification of this event is also possible for the LIGO L1 detector data with an additional pre-processing step, without the need of removing the large glitch in the final stages of the inspiral. The detection of the GW190425 event is less successful, which attests to performance degradation with the signal-to-noise ratio. Our study indicates that the YOLOv5 model is an interesting approach for first-stage detection alarm pipelines and, when integrated in more complex pipelines, for real-time inference of physical source parameters.

Machine-Learning Love: classifying the equation of state of neutron stars with Transformers

“…The development of fast ML alternatives to the computationally demanding Bayesian inference approaches has been remarkably established by [33] applying conditional variational autoencoders to BBH signals. ML approaches based on object detection have also been applied successfully in the task of detecting BNS coalescence events from the GW data of current detectors [34]. Motivated by this growing body of work, here the purpose is to study the potential to infer the NS EOS directly from simulated BNS inspiral signals using a ML approach.…”