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.

Enabling real-time multi-messenger astrophysics discoveries with deep learning

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics

Deep learning methods for enabling real-time gravitational wave and multimessenger astrophysics

A new era of gravitational wave (GW) astronomy has begun with the recent detections by LIGO. However, we need real-time observations of GW signals and their electromagnetic (EM) and astro-particle counterparts to unlock its full potential for scientific discoveries. Extracting and classifying the wide range of modeled and unmodeled GWs, whose amplitudes are often much weaker than the background noise, and rapidly inferring accurate parameters of their source is crucial in enabling this scenario of real-time multimessenger astrophysics. Identifying and automatically clustering anomalous non-Gaussian transient noises (glitches) that frequently contaminate the data and separating them from true GW signals is yet another difficult challenge. Currently, the most sensitive data analysis pipelines are limited by the extreme computational costs of template-matching methods and thus are unable to scale to all types of GW sources and their full parameter space. Accurate numerical models of GW signals covering the entire range of parameters including eccentric and spin-precessing compact binaries, which are essential to infer the astrophysical parameters of an event, are not available. Searches for unmodeled and anomalous signals do not have sufficient sensitivity compared to the targeted searches. Furthermore, existing search pipelines are not optimal for dealing with the non-stationary, non-Gaussian noise in the detectors. This indicates that many critical events will go unnoticed. The primary objective of this thesis is to resolve these issues via deep learning, a state-of-the-art machine learning method based on artificial neural networks. In this thesis we develop robust GW analysis algorithms for analyzing real LIGO/Virgo data based on deep learning with neural networks, that overcomes many limitations of existing techniques, allowing real-time detection and parameter estimation modeled GW sources and unmodeled GW bursts as well as classification and unsupervised clustering of anomalies and glitches in the detectors. This pipeline is designed to be highly scalable, therefore it can be trained with template banks of any size to cover the entire parameter-space of eccentric and spin-precessing black hole binaries as well as other sources and also optimized based on the real-time characteristics of the complex noise in the GW detectors. This deep learning framework may also be extended for low-latency analysis of the raw big data collected across multiple observational instruments to further facilitate real-time multimessenger astrophysics, which promises groundbreaking scientific insights about the origin, evolution, and destiny of the universe. In addition, this work introduces a new paradigm to accelerate scientific discovery by using data derived from high-performance physics simulations on supercomputers to train artificial intelligence algorithms that exploit emerging hardware architectures

The applications of deep neural networks to sdBV classification

With several new large-scale surveys on the horizon, including LSST, TESS, ZTF, and Evryscope, faster and more accurate analysis methods will be required to adequately process the enormous amount of data produced. Deep learning, used in industry for years now, allows for advanced feature detection in minimally prepared datasets at very high speeds; however, despite the advantages of this method, its application to astrophysics has not yet been extensively explored. This dearth may be due to a lack of training data available to researchers. Here we generate synthetic data loosely mimicking the properties of acoustic mode pulsating stars and we show that two separate paradigms of deep learning - the Artificial Neural Network And the Convolutional Neural Network - can both be used to classify this synthetic data effectively. And that additionally this classification can be performed at relatively high levels of accuracy with minimal time spent adjusting network hyperparameters.Comment: 12 pages, 10 figures, originally presented at sdOB