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.
Highlights
Matched filtering techniques [1,2,3,4] have proven highly successful in discovering binary black hole coalescences from the recordings of the Advanced LIGO and Advanced Virgo gravitational-wave observatories [5,6,7,8,9,10,11]
We investigate whether some of these challenges can efficiently be overcome by using deep convolutional neural networks (CNNs)
We pay particular attention to realistic data generation, an appropriate, task-specific architecture design and adequately chosen performance metrics. This results in the following main contributions: (1) We provide an in-depth analysis of the challenges one may expect machine learning to solve within the scope of a search for GWs from compact binary coalescences (CBCs), and discuss their limitations in replacing matched filtering or Bayesian parameter estimation techniques
Summary
Matched filtering techniques [1,2,3,4] have proven highly successful in discovering binary black hole coalescences from the recordings of the Advanced LIGO and Advanced Virgo gravitational-wave observatories [5,6,7,8,9,10,11]. CNNs have been studied in the literature as a tool for gravitational-wave searches, and previous works have shown that they can be effectively applied to this problem when treating it as a binary (i.e., two-class) classification task [29,30]. The main goal of this work is, to carefully and realistically analyze the practical potential of using CNNs to search for GWs from CBCs. Here, we pay particular attention to realistic data generation, an appropriate, task-specific architecture design and adequately chosen performance metrics.
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