http://arxiv.org/abs/1907.06917
Many potential sources of gravitational waves still await for detection. Among them, particular attention is given to a non-axisymmetric neutron star. The emitted, almost monochromatic signal, is expected to be detected in the near future by LIGO and Virgo detectors. Although the gravitational waves waveform is well known, its small amplitude makes it extremely hard to detect. The accepted approach in searching for continuous gravitational waves is a matched filter technique, known as the F-statistic method. The method consists in cross correlation of the collected data stream with signal templates in the frequency domain. Thus, for an all-sky search in which the parameters of the sources are not known, large number of templates have to be checked and therefore a large number of candidate gravitational-wave signals is produced and further analyzed. In this work, we propose deep learning as a fast method of classification for various types of candidates. We consider three types of signals: the Gaussian noise, the continuous gravitational wave, and the stationary line mimicking local artifacts in the detector. We demonstrate one and two-dimensional implementations of a convolutional neural network classifier. We present the limitations of our model with respect to the various signal-to-noise ratios and frequencies of the signal. The following work presents deep learning as a supporting method for the matched filtering detection pipeline.
F. Morawski, M. Bejger and P. Ciecieląg
Wed, 17 Jul 19
70/75
Comments: 10 pages, 11 figures, submitted to the PRD