Abstract

The sensitivity of wide-parameter-space searches for continuous gravitational waves is limited by computational cost. Recently it was shown that Deep Neural Networks (DNNs) can perform all-sky searches directly on (single-detector) strain data, potentially providing a low-computing-cost search method that could lead to a better overall sensitivity. Here we expand on this study in two respects: (i) using (simulated) strain data from two detectors simultaneously, and (ii) training for directed (i.e.\ single sky-position) searches in addition to all-sky searches. For a data timespan of $T = 10^5\, s$, the all-sky two-detector DNN is about $7\%$ less sensitive (in amplitude $h_0$) at low frequency ($f=20\,Hz$), and about $51\,\%$ less sensitive at high frequency ($f=1000\,Hz$) compared to fully-coherent matched-filtering (using WEAVE). In the directed case the sensitivity gap compared to matched-filtering ranges from about $7-14\%$ at $f=20\,Hz$ to about $37-49\%$ at $f=1500\,Hz$. Furthermore we assess the DNN's ability to generalize in signal frequency, spindown and sky-position, and we test its robustness to realistic data conditions, namely gaps in the data and using real LIGO detector noise. We find that the DNN performance is not adversely affected by gaps in the test data or by using a relatively undisturbed band of LIGO detector data instead of Gaussian noise. However, when using a more disturbed LIGO band for the tests, the DNN's detection performance is substantially degraded due to the increase in false alarms, as expected.

Highlights

  • Observing gravitational waves from compact binary mergers has become routine [1,2,3,4,5]

  • The sensitivity of continuous gravitational waves (CWs) searches is typically limited by the prohibitive computing cost

  • In this work we study the feasibility of deep neural networks (DNNs) as an alternative search method

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Summary

INTRODUCTION

Observing gravitational waves from compact binary mergers has become routine [1,2,3,4,5]. The method of training a DNN, called deep learning, has been established to be able to detect gravitational waves directly from strain data [22,23,24,25,26,27,28] for signals from mergers of compact objects.

COMPARISON TEST BENCHMARKS
DEEP-LEARNING CWs
Finding a network architecture
DNN training and validation
CHARACTERIZING DNN PERFORMANCE ON GAUSSIAN NOISE
Detection probabilities at fixed false alarm
Generalization
Frequency
Signal strength
Spin-downs
Sky position
TESTING NETWORK PERFORMANCE ON REAL DATA
Gaussian noise with data gaps
Performance on real detector data
Findings
DISCUSSION

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