English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

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

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.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0004-2EF7-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-D7EE-E
Genre: Journal Article

Files

show Files
hide Files
:
1904.08693.pdf (Preprint), 2MB
Name:
1904.08693.pdf
Description:
File downloaded from arXiv at 2019-07-11 08:53
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
:
PhysRevD.100.063015.pdf (Publisher version), 2MB
Name:
PhysRevD.100.063015.pdf
Description:
Open Access
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show

Creators

show
hide
 Creators:
Gebhard, Timothy D., Author
Kilbertus, Niki, Author
Harry, Ian1, Author              
Schölkopf, Bernhard, Author
Affiliations:
1Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society, ou_1933290              

Content

show
hide
Free keywords: 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.

Details

show
hide
Language(s):
 Dates: 2019-04-182019
 Publication Status: Published in print
 Pages: The first two authors contributed equally
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: arXiv: 1904.08693
URI: http://arxiv.org/abs/1904.08693
DOI: 10.1103/PhysRevD.100.063015
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Physical Review D
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 100 (6) Sequence Number: 063015 Start / End Page: - Identifier: -