Hierarchical approach to matched filtering using a reduced basis

Searching for gravitational waves from compact binary coalescences (CBC) is performed by matched filtering the observed strain data from gravitational-wave observatories against a discrete set of waveform templates designed to accurately approximate the expected gravitational-wave signal, and are chosen to efficiently cover a target search region. The computational cost of matched filtering scales with both the number of templates required to cover a parameter space and the in-band duration of the waveform. Both of these factors increase in difficulty as the current observatories improve in sensitivity, especially at low frequencies, and may pose challenges for third-generation observatories. Reducing the cost of matched filtering would make searches of future detector's data more tractable. In addition, it would be easier to conduct searches that incorporate the effects of eccentricity, precession or target light sources (e.g. subsolar). We present a hierarchical scheme based on a reduced bases method to decrease the computational cost of conducting a matched-filter based search. Compared to the current methods, we estimate without any loss in sensitivity, a speedup by a factor of $\sim$ 18 for sources with signal-to-noise ratio (SNR) of at least $= 6.0$, and a factor of $8$ for SNR of at least 5. Our method is dominated by linear operations which are highly parallelizable. Therefore, we implement our algorithm using graphical processing units (GPUs) and evaluate commercially motivated metrics to demonstrate the efficiency of GPUs in CBC searches. Our scheme can be extended to generic CBC searches and allows for efficient matched filtering using GPUs

Detecting and Denoising Gravitational Wave Signals from Binary Black Holes using Deep Learning

We present a convolutional neural network, designed in the auto-encoder configuration that can detect and denoise astrophysical gravitational waves from merging black hole binaries, orders of magnitude faster than the conventional matched-filtering based detection that is currently employed at advanced LIGO (aLIGO). The Neural-Net architecture is such that it learns from the sparse representation of data in the time-frequency domain and constructs a non-linear mapping function that maps this representation into two separate masks for signal and noise, facilitating the separation of the two, from raw data. This approach is the first of its kind to apply machine learning based gravitational wave detection/denoising in the 2D representation of gravitational wave data. We applied our formalism to the first gravitational wave event detected, GW150914, successfully recovering the signal at all three phases of coalescence at both detectors. This method is further tested on the gravitational wave data from the second observing run ($O2$) of aLIGO, reproducing all binary black hole mergers detected in $O2$ at both the aLIGO detectors. The Neural-Net seems to have uncovered a pattern of 'ringing' after the ringdown phase of the coalescence, which is not a feature that is present in the conventional binary merger templates. This method can also interpolate and extrapolate between modeled templates and explore gravitational waves that are unmodeled and hence not present in the template bank of signals used in the matched-filtering detection pipelines. Faster and efficient detection schemes, such as this method, will be instrumental as ground based detectors reach their design sensitivity, likely to result in several hundreds of potential detections in a few months of observing runs.Comment: 15 pages, 11 figure

Application des réseaux neuronaux à la recherche d’ondes gravitationnelles émises par des systèmes légers

With GW170817, gravitational waves have shown themselves to be very useful for multi-messenger astronomy. Combining the information from multiple channels such as gravitational waves, gamma-rays, neutrinos, etc. can lead to great physics. Contrarily to the electromagnetic telescopes, a gravitational wave interferometer surveys the entire sky. They do not have to focus on a small portion of the celestial sphere as do standard telescopes. It is also known that for binary neutron stars, the electromagnetic counterpart is produced during the last phase of the merger, whereas the gravitational wave signal can be detected several minutes before these last stages. If one is able to detect this signal before the merger and infer the sky location, gravitational wave astronomy can then send an alert and produce a sky map indicating where the astronomer can point their telescopes to see an electromagnetic counterpart. The standard technique to detect these compact binary coalescences is matched filtering. The principle is to compute a template bank of pre-computed waveforms and match them with the data strain coming from the LIGO and Virgo interferometers. This thesis starts by illustrating a matched filter search with a project to detect long signals coming from sub-solar coalescence. Recently, some matched filtering pipelines have started to adapt their method to search for gravitational waves with only the early stage of the signal. Other methods are beginning to be developed for this type of research. This thesis presents new methods, based on machine learning, to detect the early phase of a binary neutron star merger. We have developed multiple convolutional neural networks looking directly at the strain data of the detector to detect binary neutron stars before the merger. The last step to produce an early warning for the astronomer is to create a sky map indicating the location of the event. We therefore shortly discuss how to accomplish this through a machine learning method for the whole signal, and also mention how it can be adapted to the early part of the signal

Machine learning to extract gravitational wave transients

Convolutional Neural Networks (CNNs) have demonstrated potential for the real-time analysis of data from gravitational-wave detector networks for the specific case of signals from coalescing compact-object binaries such as black-hole binaries. In this thesis we present the development of machine learning pipeline named MLy. We demonstrate a CNN with the ability to detect generic signals - those without a precise model - with sensitivity across a wide parameter space. In this endeavour we utilised the information of correlation between detectors, rather than signal morphologies, to distinguish correlated gravitational-wave signals from uncorrelated noise transients. We demonstrate the efficacy of our CNN using data from the second LIGO-Virgo observing run. We show that it has sensitivity approaching that of the "gold-standard" unmodeled transient searches currently used by LIGO-Virgo, at extremely low (order of 1 second) latency and using only a fraction of the computing power required by existing searches, allowing our models the possibility of true real-time detection of gravitational-wave transients associated with gamma-ray bursts, core-collapse supernovae, and other relativistic astrophysical phenomena

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

Gravitational waves with gamma-ray bursts

Gravitational waves have now twice been detected emanating from the merging of binary black hole systems. In this thesis we detail the methods used to search for binary merger gravitational wave signals associated with short gamma-ray bursts, focusing on systems that include at least one neutron star. We �rst cover the background theory behind gravitational wave emission, the means of detection via interferometry, and the types of astrophysical sources that could be detected now or in the near future. We follow this with a review of gamma-ray burst theory and observations, focusing in particular those bursts with short durations. These are likely to be caused by the mergers of binaries that include a neutron star and a black hole, or two neutron stars — events of great interest to gravitational wave astronomy. We then discuss the methods used to search gravitational wave data in a targeted way, using the prior observation of a short gamma-ray bursts to focus the analysis and improve the chances of making a detection. We also summarise early searches of this kind and present the results of a search carried out on LIGO and Virgo data spanning 2005–2010, targeting short gamma-ray bursts detected by the InterPlanetary Network. We then turn our attention to the current, second generation of gravitational wave detectors. We present a detailed calculation of the prospects of success for the targeted short gamma-ray burst search technique, and �nd that we might reasonably expect to make up to a few detections per year around the turn of the decade. We then outline a new search structure for use during the second generation of detectors, and an astrophysical event alert system for the control rooms of gravitational wave observatories. We end with a presentation of the results of the new and improved search carried out during the �rst observing run of Advanced LIGO

Gravitational waves with gamma-ray bursts

Gravitational waves have now twice been detected emanating from the merging of binary black hole systems. In this thesis we detail the methods used to search for binary merger gravitational wave signals associated with short gamma-ray bursts, focusing on systems that include at least one neutron star. We first cover the background theory behind gravitational wave emission, the means of detection via interferometry, and the types of astrophysical sources that could be detected now or in the near future. We follow this with a review of gamma-ray burst theory and observations, focusing in particular those bursts with short durations. These are likely to be caused by the mergers of binaries that include a neutron star and a black hole, or two neutron stars — events of great interest to gravitational wave astronomy. We then discuss the methods used to search gravitational wave data in a targeted way, using the prior observation of a short gamma-ray bursts to focus the analysis and improve the chances of making a detection. We also summarise early searches of this kind and present the results of a search carried out on LIGO and Virgo data spanning 2005–2010, targeting short gamma-ray bursts detected by the InterPlanetary Network. We then turn our attention to the current, second generation of gravitational wave detectors. We present a detailed calculation of the prospects of success for the targeted short gamma-ray burst search technique, and find that we might reasonably expect to make up to a few detections per year around the turn of the decade. We then outline a new search structure for use during the second generation of detectors, and an astrophysical event alert system for the control rooms of gravitational wave observatories. We end with a presentation of the results of the new and improved search carried out during the first observing run of Advanced LIGO

Present and Future of Gravitational Wave Astronomy

The first detection on Earth of a gravitational wave signal from the coalescence of a binary black hole system in 2015 established a new era in astronomy, allowing the scientific community to observe the Universe with a new form of radiation for the first time. More than five years later, many more gravitational wave signals have been detected, including the first binary neutron star coalescence in coincidence with a gamma ray burst and a kilonova observation. The field of gravitational wave astronomy is rapidly evolving, making it difficult to keep up with the pace of new detector designs, discoveries, and astrophysical results. This Special Issue is, therefore, intended as a review of the current status and future directions of the field from the perspective of detector technology, data analysis, and the astrophysical implications of these discoveries. Rather than presenting new results, the articles collected in this issue will serve as a reference and an introduction to the field. This Special Issue will include reviews of the basic properties of gravitational wave signals; the detectors that are currently operating and the main sources of noise that limit their sensitivity; planned upgrades of the detectors in the short and long term; spaceborne detectors; a data analysis of the gravitational wave detector output focusing on the main classes of detected and expected signals; and implications of the current and future discoveries on our understanding of astrophysics and cosmology