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Journal Article

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

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Harry,  Ian
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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1904.08693.pdf
(Preprint), 2MB

PhysRevD.100.063015.pdf
(Publisher version), 2MB

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Citation

Gebhard, T. D., Kilbertus, N., Harry, I., & Schölkopf, B. (2019). Convolutional neural networks: a magic bullet for gravitational-wave detection? Physical Review D, 100(6): 063015. doi:10.1103/PhysRevD.100.063015.


Cite as: http://hdl.handle.net/21.11116/0000-0004-2EF7-3
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 can not 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.