Convolutional neural network classifier for the output of the time-domain -statistic all-sky search for continuous gravitational waves

Filip Morawski,Paweł Ciecieląg,Michał Bejger

doi:10.1088/2632-2153/ab86c7

Abstract

Among the astrophysical sources in the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) and Advanced Virgo detectors’ frequency band are rotating non-axisymmetric neutron stars emitting long-lasting, almost-monochromatic gravitational waves. Searches for these continuous gravitational-wave signals are usually performed in long stretches of data in a matched-filter framework e.g. the -statistic method. In an all-sky search for a priori unknown sources, a large number of templates are matched against the data using a pre-defined grid of variables (the gravitational-wave frequency and its derivatives, sky coordinates), subsequently producing a collection of candidate signals, corresponding to the grid points at which the signal reaches a pre-defined signal-to-noise threshold. An astrophysical signature of the signal is encoded in the multi-dimensional vector distribution of the candidate signals. In the first work of this kind, we apply a deep learning approach to classify the distributions. We consider three basic classes: Gaussian noise, astrophysical gravitational-wave signal, and a constant-frequency detector artifact (‘stationary line’), the two latter injected into the Gaussian noise. 1D and 2D versions of a convolutional neural network classifier are implemented, trained and tested on a broad range of signal frequencies. We demonstrate that these implementations correctly classify the instances of data at various signal-to-noise ratios and signal frequencies, while also showing concept generalization i.e. satisfactory performance at previously unseen frequencies. In addition we discuss the deficiencies, computational requirements and possible applications of these implementations.

Highlights

IntroductionIn addition to merging binary systems, among other promising sources of Gravitational wave searchesGravitational waves (GWs) are non-axisymmetric supernova explosions, as well as long-lived, almost-monochromatic GW emission by rotating, non-axisymmetric neutron star (NS), sometimes called the “GW pulsars”
The results shown on the left figure are corresponding to models trained and tested on 1D data representation, whereas the results shown on the right plot refer to 2D data representation
We proved that the convolutional neural network (CNN) can be successfully applied in the classification of TD-Fstat search results, multidimensional vector distributions corresponding to three signal types: the Gravitational wave searchesGravitational waves (GWs) signal, the stationary line and the noise

Summary

Introduction

In addition to merging binary systems, among other promising sources of GWs are non-axisymmetric supernova explosions, as well as long-lived, almost-monochromatic GW emission by rotating, non-axisymmetric NS, sometimes called the “GW pulsars”. The departure from axisymmetry in the mass distribution of a rotating NS can be caused by dense-matter instabilities (e.g., phase transitions, r-modes), strong magnetic fields and/or elastic stresses in its interior (for a review see [9, 10]). The deformation and the amplitude of the GW signal depends on the largely unknown dense-matter equation of state, surrounding and history of the NS, the time-varying mass quadrupole required by the GW emission is not naturally guaranteed as in the case of binary system mergers. The LIGO and Virgo collaborations performed several searches for such signals, both targeted searches for NS sources of known spin frequency parameters and sky coordinates (pulsars, [11, 12] and references therein), as well as all-sky searches for a priori unknown sources with unknown parameters ([13, 14] and references therein)

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