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

Advancing the search for gravitational waves using machine learning

Over 100 years ago Einstein formulated his now famous theory of General Relativity. In his theory he lays out a set of equations which lead to the beginning of a brand-new astronomical field, Gravitational wave (GW) astronomy. The LIGO-Virgo-KAGRA Collaboration (LVK)’s aim is the detection of GW events from some of the most violent and cataclysmic events in the known universe. The LVK detectors are composed of large-scale Michelson Morley interferometers which are able to detect GWs from a range of sources including: binary black holes (BBHs), binary neutron stars (BNSs), neutron star black holes (NSBHs), supernovae and stochastic GWs. Although these GW events release an incredible amount of energy, the amplitudes of the GWs from such events are also incredibly small. The LVK uses sophisticated techniques such as matched filtering and Bayesian inference in order to both detect and infer source parameters from GW events. Although optimal under many circumstances, these standard methods are computationally expensive to use. Given that the expected number of GW detections by the LVK will be of order 100s in the coming years, there is an urgent need for less computationally expensive detection and parameter inference techniques. A possible solution to reducing the computational expense of such techniques is the exciting field of machine learning (ML). In the first chapter of this thesis, GWs are introduced and it is explained how GWs are detected by the LVK. The sources of GWs are given, as well as methodologies for detecting various source types, such as matched filtering. In addition to GW signal detection techniques, the methods for estimating the parameters of detected GW signals is described (i.e. Bayesian inference). In the second chapter several machine learning algorithms are introduced including: perceptrons, convolutional neural networks (CNNs), autoencoders (AEs), variational autoencoders (VAEs) and conditional variational autoencoders (CVAEs). Practical advice on training/data augmentation techniques is also provided to the reader. In the third chapter, a survey on several ML techniques applied a variety of GW problems are shown. In this thesis, various ML and statistical techniques were deployed such as CVAEs and CNNs in two first-of-their-kind proof-of-principle studies. In the fourth chapter it is described how a CNN may be used to match the sensitivity of matched filtering, the standard technique used by the LVK for detecting GWs. It was shown how a CNN may be trained using simulated BBH waveforms buried in Gaussian noise and signals with Gaussian noise alone. Results of the CNN classification predictions were compared to results from matched filtering given the same testing data as the CNN. In the results it was demonstrated through receiver operating characteristics and efficiency curves that the ML approach is able to achieve the same levels of sensitivity as that of matched filtering. It is also shown that the CNN approach is able to generate predictions in low-latency. Given approximately 25000 GW time series, the CNN is able to produce classification predictions for all 25000 in 1s. In the fifth and sixth chapters, it is shown how CVAEs may be used in order to perform Bayesian inference. A CVAE was trained using simulated BBH waveforms in Gaussian noise, as well as the source parameter values of those waveforms. When testing, the CVAE is only supplied the BBH waveform and is able to produce samples from the Bayesian posterior. Results were compared to that of several standard Bayesian samplers used by the LVK including: Dynesty, ptemcee, emcee, and CPnest. It is shown that when properly trained the CVAE method is able to produce Bayesian posteriors which are consistent with other Bayesian samplers. Results are quantified using a variety of figures of merit such as probability-probability (p-p) plots in order to check the 1-dimensional marginalised posteriors from all approaches are self-consistent with the frequentist perspective. The Jensen—Shannon (JS)-divergence was also employed in order to compute the similarity of different posterior distributions from one another, as well as other figures of merit. It was also demonstrated that the CVAE model was able to produce posteriors with 8000 samples in under a second, representing a 6 order of magnitude increase in performance over traditional sampling methods

A Study on the Characterization and Implementation of Tools for Advanced LIGO

The Laser Interferometer Gravitational Wave Observatory (LIGO) is aimed at directly detecting gravitational waves, small perturbations or ripples in the fabric of space-time. Because of their extreme sensitivity, the LIGO detectors are affected by many sources of non-astrophysical noise. In the first part of this thesis we test a pipeline designed for the identification of short-duration noise transients, called Omicron. We first inject simulated noise waveforms in engineering run data from the LIGO detector in Livingston, Louisiana and then determine Omicron efficiency by attempting to recover these injections. In the second part of this thesis, we present a novel method for the characterization of signals in LIGO data. Using data from LIGO’s sixth science run, we develop an algorithm to classify noise transients by their morphology, as well as other parameters such as signal-to-noise ratio, duration, and bandwidth. Two methods, the Kohonen self organizing feature maps and the discrete wavelet transform coefficients, are used to reduce the multidimensional trigger set into an easily readable two-dimensional format

Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy

Gravitational wave (GW) detection is now commonplace and as the sensitivity of the global network of GW detectors improves, we will observe $\mathcal{O}(100)$s of transient GW events per year. The current methods used to estimate their source parameters employ optimally sensitive but computationally costly Bayesian inference approaches where typical analyses have taken between 6 hours and 5 days. For binary neutron star and neutron star black hole systems prompt counterpart electromagnetic (EM) signatures are expected on timescales of 1 second -- 1 minute and the current fastest method for alerting EM follow-up observers, can provide estimates in $\mathcal{O}(1)$ minute, on a limited range of key source parameters. Here we show that a conditional variational autoencoder pre-trained on binary black hole signals can return Bayesian posterior probability estimates. The training procedure need only be performed once for a given prior parameter space and the resulting trained machine can then generate samples describing the posterior distribution $\sim 6$ orders of magnitude faster than existing techniques.Comment: 13 pages, 5 figure

Limiting the effects of earthquakes on gravitational-wave interferometers

Ground-based gravitational wave interferometers such as the Laser Interferometer Gravitational-wave Observatory (LIGO) are susceptible to high-magnitude teleseismic events, which can interrupt their operation in science mode and significantly reduce the duty cycle. It can take several hours for a detector to stabilize enough to return to its nominal state for scientific observations. The down time can be reduced if advance warning of impending shaking is received and the impact is suppressed in the isolation system with the goal of maintaining stable operation even at the expense of increased instrumental noise. Here we describe an early warning system for modern gravitational-wave observatories. The system relies on near real-time earthquake alerts provided by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). Hypocenter and magnitude information is generally available in 5 to 20 minutes of a significant earthquake depending on its magnitude and location. The alerts are used to estimate arrival times and ground velocities at the gravitational-wave detectors. In general, 90\% of the predictions for ground-motion amplitude are within a factor of 5 of measured values. The error in both arrival time and ground-motion prediction introduced by using preliminary, rather than final, hypocenter and magnitude information is minimal. By using a machine learning algorithm, we develop a prediction model that calculates the probability that a given earthquake will prevent a detector from taking data. Our initial results indicate that by using detector control configuration changes, we could prevent interruption of operation from 40-100 earthquake events in a 6-month time-period

Mimicking mergers: mistaking black hole captures as mergers

As the number of gravitational wave observations has increased in recent years, the variety of sources has broadened. Here, we investigate whether it is possible for the current generation of detectors to distinguish between very short-lived gravitational wave signals from mergers between high-mass black holes and the signal produced by a close encounter between two black holes, which results in gravitational capture and ultimately a merger. We compare the posterior probability distributions produced by analysing simulated signals from both types of progenitor events, both under ideal and realistic scenarios. We show that while under ideal conditions it is possible to distinguish both progenitors, under realistic conditions they are indistinguishable. This has important implications for the interpretation of such short signals, and we therefore advocate that these signals be the focus of additional investigation even when satisfactory results have been achieved from standard analyses

Ground motion prediction at gravitational wave observatories using archival seismic data

Gravitational wave observatories have always been affected by tele-seismic earthquakes leading to a decrease in duty cycle and coincident observation time. In this analysis, we leverage the power of machine learning algorithms and archival seismic data to predict the ground motion and the state of the gravitational wave interferometer during the event of an earthquake. We demonstrate improvement from a factor of 5 to a factor of 2.5 in scatter of the error in the predicted ground velocity over a previous model fitting based approach. The level of accuracy achieved with this scheme makes it possible to switch control configuration during periods of excessive ground motion thus preventing the interferometer from losing lock. To further assess the accuracy and utility of our approach, we use IRIS seismic network data and obtain similar levels of agreement between the estimates and the measured amplitudes. The performance indicates that such an archival or prediction scheme can be extended beyond the realm of gravitational wave detector sites for hazard-based early warning alerts.Comment: 10 pages, 7 figures; matches published versio

Incomplete inhibition of central postural commands during manual motor imagery

Imagined movements exhibit many of the behavioral and neurophysiological characteristics of executed actions. As a result, they are considered simulations of physical actions with an inhibition mechanism that suppresses overt movement. This inhibition is incomplete, as it does not block autonomic preparation, and it also does not effectively suppress postural adjustments planned in support of imagined movements. It has been suggested that a central inhibition command may fail to suppress postural adjustments because it may not have access to afference-based elaborations of the postural response that occur downstream of central motor planning. Here, we measured changes in the postural response associated with imagining manual reaching movements under varying levels of imagined loading of the arm. We also manipulated stance stability, and found that postural sway reduced with increased (imagined) arm loading when imagining reaching movements from the less stable stance. As there were no afferent signals associated with the loading constraint, these results suggest that postural adjustments can leak during motor imagery because the postural component of the central motor plan is itself not inhibited effectively

Ground motion prediction at gravitational wave observatories using archival seismic data

International audienceGravitational wave observatories have always been affected by tele-seismic earthquakes leading to a decrease in duty cycle and coincident observation time. In this analysis, we leverage the power of machine learning algorithms and archival seismic data to predict the ground motion and the state of the gravitational wave interferometer during the event of an earthquake. We demonstrate improvement from a factor of 5 to a factor of 2.5 in scatter of the error in the predicted ground velocity over a previous model fitting based approach. The level of accuracy achieved with this scheme makes it possible to switch control configuration during periods of excessive ground motion thus preventing the interferometer from losing lock. To further assess the accuracy and utility of our approach, we use IRIS seismic network data and obtain similar levels of agreement between the estimates and the measured amplitudes. The performance indicates that such an archival or prediction scheme can be extended beyond the realm of gravitational wave detector sites for hazard-based early warning alerts