788 research outputs found
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
The recent Nobel-prize-winning detections of gravitational waves from merging
black holes and the subsequent detection of the collision of two neutron stars
in coincidence with electromagnetic observations have inaugurated a new era of
multimessenger astrophysics. To enhance the scope of this emergent field of
science, we pioneered the use of deep learning with convolutional neural
networks, that take time-series inputs, for rapid detection and
characterization of gravitational wave signals. This approach, Deep Filtering,
was initially demonstrated using simulated LIGO noise. In this article, we
present the extension of Deep Filtering using real data from LIGO, for both
detection and parameter estimation of gravitational waves from binary black
hole mergers using continuous data streams from multiple LIGO detectors. We
demonstrate for the first time that machine learning can detect and estimate
the true parameters of real events observed by LIGO. Our results show that Deep
Filtering achieves similar sensitivities and lower errors compared to
matched-filtering while being far more computationally efficient and more
resilient to glitches, allowing real-time processing of weak time-series
signals in non-stationary non-Gaussian noise with minimal resources, and also
enables the detection of new classes of gravitational wave sources that may go
unnoticed with existing detection algorithms. This unified framework for data
analysis is ideally suited to enable coincident detection campaigns of
gravitational waves and their multimessenger counterparts in real-time.Comment: 6 pages, 7 figures; First application of deep learning to real LIGO
events; Includes direct comparison against matched-filterin
Convolutional neural networks: a magic bullet for gravitational-wave detection?
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.Comment: First two authors contributed equally; appeared at Phys. Rev.
Machine-learning nonstationary noise out of gravitational-wave detectors
Signal extraction out of background noise is a common challenge in high-precision physics experiments, where the measurement output is often a continuous data stream. To improve the signal-to-noise ratio of the detection, witness sensors are often used to independently measure background noises and subtract them from the main signal. If the noise coupling is linear and stationary, optimal techniques already exist and are routinely implemented in many experiments. However, when the noise coupling is nonstationary, linear techniques often fail or are suboptimal. Inspired by the properties of the background noise in gravitational wave detectors, this work develops a novel algorithm to efficiently characterize and remove nonstationary noise couplings, provided there exist witnesses of the noise source and of the modulation. In this work, the algorithm is described in its most general formulation, and its efficiency is demonstrated with examples from the data of the Advanced LIGO gravitational-wave observatory, where we could obtain an improvement of the detector gravitational-wave reach without introducing any bias on the source parameter estimation
Image-based deep learning for classification of noise transients in gravitational wave detectors
The detection of gravitational waves has inaugurated the era of gravitational
astronomy and opened new avenues for the multimessenger study of cosmic
sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo
interferometers will probe a much larger volume of space and expand the
capability of discovering new gravitational wave emitters. The characterization
of these detectors is a primary task in order to recognize the main sources of
noise and optimize the sensitivity of interferometers. Glitches are transient
noise events that can impact the data quality of the interferometers and their
classification is an important task for detector characterization. Deep
learning techniques are a promising tool for the recognition and classification
of glitches. We present a classification pipeline that exploits convolutional
neural networks to classify glitches starting from their time-frequency
evolution represented as images. We evaluated the classification accuracy on
simulated glitches, showing that the proposed algorithm can automatically
classify glitches on very fast timescales and with high accuracy, thus
providing a promising tool for online detector characterization.Comment: 25 pages, 8 figures, accepted for publication in Classical and
Quantum Gravit
Matching matched filtering with deep networks in gravitational-wave astronomy
We report on the construction of a deep convolutional neural network that can
reproduce the sensitivity of a matched-filtering search for binary black hole
gravitational-wave signals. The standard method for the detection of well
modeled transient gravitational-wave signals is matched filtering. However, the
computational cost of such searches in low latency will grow dramatically as
the low frequency sensitivity of gravitational-wave detectors improves.
Convolutional neural networks provide a highly computationally efficient method
for signal identification in which the majority of calculations are performed
prior to data taking during a training process. We use only whitened time
series of measured gravitational-wave strain as an input, and we train and test
on simulated binary black hole signals in synthetic Gaussian noise
representative of Advanced LIGO sensitivity. We show that our network can
classify signal from noise with a performance that emulates that of match
filtering applied to the same datasets when considering the sensitivity defined
by Reciever-Operator characteristics.Comment: 5 pages, 3 figures, submitted to PR
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