• Open Access

Convolutional neural networks: A magic bullet for gravitational-wave detection?

Timothy D. Gebhard, Niki Kilbertus, Ian Harry, and Bernhard Schölkopf
Phys. Rev. D 100, 063015 – Published 26 September 2019

Abstract

In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the gravitational-wave signature of merging black holes. However, to date, these methods have not yet been successfully applied to the analysis of long stretches of data recorded by the Advanced LIGO and Virgo gravitational-wave observatories. In this work, we critically examine the use of convolutional neural networks as a tool to search for merging black holes. We identify the strengths and limitations of this approach, highlight some common pitfalls in translating between machine learning and gravitational-wave astronomy, and discuss the interdisciplinary challenges. In particular, we explain in detail why convolutional neural networks alone cannot be used to claim a statistically significant gravitational-wave detection. However, we demonstrate how they can still be used to rapidly flag the times of potential signals in the data for a more detailed follow-up. Our convolutional neural network architecture as well as the proposed performance metrics are better suited for this task than a standard binary classifications scheme. A detailed evaluation of our approach on Advanced LIGO data demonstrates the potential of such systems as trigger generators. Finally, we sound a note of caution by constructing adversarial examples, which showcase interesting “failure modes” of our model, where inputs with no visible resemblance to real gravitational-wave signals are identified as such by the network with high confidence.

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  • Received 25 April 2019

DOI:https://doi.org/10.1103/PhysRevD.100.063015

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Gravitation, Cosmology & Astrophysics

Authors & Affiliations

Timothy D. Gebhard1,2,*, Niki Kilbertus1,3,†, Ian Harry4,5, and Bernhard Schölkopf1

  • 1Max Planck Institute for Intelligent Systems, Max-Planck-Ring 4, 72076 Tübingen, Germany
  • 2Max Planck ETH Center for Learning Systems, Universitätstrasse 6, 8092 Zürich, Switzerland
  • 3Engineering Department, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom
  • 4Institute for Cosmology and Gravitation, University of Portsmouth, 1-8 Burnaby Road, Portsmouth, P01 3FZ, United Kingdom
  • 5Max Planck Institute for Gravitational Physics, Am Mühlenberg 1, 14476 Potsdam, Germany

  • *Corresponding author. tgebhard@tue.mpg.de
  • Corresponding author. nkilbertus@tue.mpg.de.

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Issue

Vol. 100, Iss. 6 — 15 September 2019

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