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Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM, Astrophysics, High Energy Astrophysical Phenomena, astro-ph.HE,Computer Science, Learning, cs.LG,Statistics, Machine Learning, stat.ML
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.
