Search

Gravitational-wave searches for signals of intermediate-mass black hole binaries are hindered by detector glitches, as the increased masses from stellar-mass systems hinder current generation detectors from observing the inspiral phase of the binary evolution. This causes the waveforms to strongly resemble glitches, which are of similar duration within a similar frequency band. Additionally, precession of the orbital plane of a binary black hole may further warp signal waveforms. In this work three neural network-based classifiers for the task of distinguishing between signals and glitches are introduced, with each following different training regimes to study the impact of precession on the classifiers. Although all classifiers show highly accurate performance, the classifier found to perform best was trained following the principle of curriculum learning, where new examples are introduced only after the mastery of easier preceding examples. This classifier obtains an accuracy of approximately 95% on a synthetic test set consisting of signals and glitches injected into coloured noise from the O3 LIGO Hanford power spectral density. The model is compared to matched filtering, the state-of-the-art in modelled gravitational-wave searches, and analysed in search of particular sensitivities to black hole binary parameters. It was found that while the classifier is affected by the total mass of a system, the prediction of a misclassification is most strongly determined by visibility through the signal-to-noise ratio. The analysis of the three classifiers demonstrates that precession is handled differently depending on the training regime, meaning the architecture is not fully robust to precession and advancements can be made through the development of training routines., Comment: 15 pages, 10 figures

The detection of gravitational waves (GWs) from binary neutron stars (BNSs) with possible telescope follow-ups opens a window to ground-breaking discoveries in the field of multi-messenger astronomy. With the improved sensitivity of current and future GW detectors, more BNS detections are expected in the future. Therefore, enhancing low-latency GW search algorithms to achieve rapid speed, high accuracy, and low computational cost is essential. One innovative solution to reduce latency is the use of machine learning (ML) methods embedded in field-programmable gate arrays (FPGAs). In this work, we present a novel \texttt{WaveNet}-based method, leveraging the state-of-the-art ML model, to produce early-warning alerts for BNS systems. Using simulated GW signals embedded in Gaussian noise from the Advanced LIGO and Advanced Virgo detectors' third observing run (O3) as a proof-of-concept dataset, we demonstrate significant performance improvements. Compared to the current leading ML-based early-warning system, our approach enhances detection accuracy from 66.81\% to 76.22\% at a 1\% false alarm probability. Furthermore, we evaluate the time, energy, and economical cost of our model across CPU, GPU, and FPGA platforms, showcasing its potential for deployment in real-time gravitational wave detection pipelines., Comment: 10 pages, 7 figures, 3 tables, submitted to IEEE Conference in Big Data 2024

Leveraging the features of the GstLAL pipeline, we present the results of a matched filtering search for asymmetric binary black hole systems with heavily misaligned spins in LIGO and Virgo data taken during the third observing run. Our target systems show strong imprints of precession whereas current searches have non-optimal sensitivity in detecting them. After measuring the sensitivity improvement brought by our search over standard spin-aligned searches, we report the detection of 30 gravitational wave events already discovered in the latest version of the Gravitational Wave Transient Catalog. However, we do not find any additional significant gravitational wave candidates. Our results allow us to place an upper limit of $R_{90\%} = 0.28^{+0.33}_{-0.04}\;\; \mathrm{Gpc^{-3}yr^{-1}}$ on the merger rate of a hypothetical subpopulation of asymmetric, heavily precessing signals, not identified by other searches. Since our upper limit is consistent with the latest rate estimates from the LIGO-Virgo-KAGRA collaboration, our findings rule out the existence of a yet-to-be-discovered population of precessing binaries.

Gravitational-wave analyses depend heavily on waveforms that model the evolution of compact binary coalescences as seen by observing detectors. In many cases these waveforms are given by waveform approximants, models that approximate the amplitude and phase of the waveform at a set of frequencies. Because of their omnipresence, improving the speed at which approximants can generate waveforms is crucial to accelerating the overall analysis of gravitational-wave detections. An optimisation algorithm is proposed that can select at which frequencies in the spectrum an approximant should compute the power of a waveform, and at which frequencies the power can be safely interpolated at a minor loss in accuracy. The algorithm used is an evolutionary algorithm modeled after the principle of natural selection, iterating frequency arrays that perform better at every iteration. As an application, the candidates proposed by the algorithm are used to reconstruct signal-to-noise ratios. It is shown that the IMRPhenomXPHM approximant can be sped up by at least 30% at a loss of at most 2.87% on the drawn samples, measured by the accuracy of the reconstruction of signal-to-noise ratios. The behaviour of the algorithm as well as lower bounds on both speedup and error are explored, leading to a proposed proof of concept candidate that obtains a speedup of 46% with a maximum error of 0.5% on a sample of the parameter space used., Comment: 15 pages, 11 figures

Precession in Binary Black Holes (BBH) is caused by the failure of the Black Hole spins to be aligned and its study can open up new perspectives in gravitational waves (GW) astronomy, providing, among other advancements, a precise measure of distance and an accurate characterization of the BBH spins. However, detecting precessing signals is a highly non-trivial task, as standard matched filtering pipelines for GW searches are built on many assumptions that do not hold in the precessing case. This work details the upgrades made to the GstLAL pipeline to facilitate the search for precessing BBH signals. The implemented changes in the search statistics and in the signal consistency test are then described in detail. The performance of the upgraded pipeline is evaluated through two extensive searches of precessing signals, targeting two different regions in the mass space, and the consistency of the results is examined. Additionally, the benefits of the upgrades are assessed by comparing the sensitive volume of the precessing searches with two corresponding traditional aligned-spin searches. While no significant sensitivity improvement is observed for precessing binaries with mass ratio $q\lesssim 6$, a volume increase of up to 100\% is attainable for heavily asymmetric systems with largely misaligned spins. Furthermore, our findings suggest that the primary cause of degraded performance in an aligned-spin search targeting precessing signals is not a poor signal-to-noise-ratio recovery but rather the failure of the $\xi^2$ signal-consistency test. Our work paves the way for a large-scale search for precessing signals, which could potentially result in exciting future detections.

We propose a novel signal-consistency test applicable to a broad search for gravitational waves emitted by generic binary black hole (BBH) systems. The test generalizes the time domain $\xi^2$ signal-consistency test currently utilized by the GstLAL pipeline, which quantifies the discrepancy between the expected signal-to-noise ratio timeseries with the measured one. While the traditional test is restricted to aligned-spin circular orbits and does not account for higher-order modes (HMs), our test does not make any assumption on the nature of the signal. After addressing the mathematical details of the new test, we quantify its advantages in the context of searching for precessing BBHs and/or BBHs with HM content. Our results reveal that for precessing signals, the new test is optimal and has the potential to reduce the values of the $\xi^2$ statistics by up to two orders of magnitude when compared to the standard test. However, in the case of signals with HM content, only a modest enhancement is observed. Recognizing the computational burden associated with the new test, we also derive an approximated signal-consistency test. This approximation maintains the same computational cost as the standard test and can be easily implemented in any matched filtering pipeline with minimal changes, sacrificing only a few percent of accuracy in the low SNR regime. However in the high SNR regime the approximated signal consistency test does not bring any improvement as compared to the "standard" one. By introducing our new test and its approximation and understanding their validity and limitation, this work will benefit any matched-filtering pipeline aimed at searching for BBH signals with strong precession and/or HM content.

Gravitational-wave (GW) interferometers are able to detect a change in distance of $\sim$ 1/10,000th the size of a proton. Such sensitivity leads to large appearance rates of non-Gaussian transient noise bursts in the main detector strain, also known as glitches. These glitches come in a wide range of frequency-amplitude-time morphologies and are caused by environmental or instrumental processes, hindering searches for all sources of gravitational waves. Current approaches for their identification use supervised models to learn their morphology in the main strain, but do not consider relevant information provided by auxiliary channels that monitor the state of the interferometers nor provide a flexible framework for novel glitch morphologies. In this work, we present an unsupervised algorithm to find anomalous glitches. We encode a subset of auxiliary channels from LIGO Livingston in the fractal dimension, a measure for the complexity of the data, and learn the underlying distribution of the data using an auto-encoder with periodic convolutions. In this way, we uncover unknown glitch morphologies, and overlaps in time between different glitches and misclassifications. This led to the discovery of anomalies in $6.6 \%$ of the input data. The results of this investigation stress the learnable structure of auxiliary channels encoded in fractal dimension and provide a flexible framework to improve the state-of-the-art of glitch identification algorithms., Comment: 14 pages, 13 figues, to be published in Classical and Quantum Gravity

Gravitational-wave searches for cosmic strings are currently hindered by the presence of detector glitches, some classes of which strongly resemble cosmic string signals. This confusion greatly reduces the efficiency of searches. A deep-learning model is proposed for the task of distinguishing between gravitational wave signals from cosmic string cusps and simulated blip glitches in design sensitivity data from the future Einstein Telescope. The model is an ensemble consisting of three convolutional neural networks, achieving an accuracy of 79%, a true positive rate of 76%, and a false positive rate of 18%. This marks the first time convolutional neural networks have been trained on a realistic population of Einstein Telescope glitches. On a dataset consisting of signals and glitches, the model is shown to outperform matched filtering, specifically being better at rejecting glitches. The behaviour of the model is interpreted through the application of several methods, including a novel technique called waveform surgery, used to quantify the importance of waveform sections to a classification model. In addition, a method to visualise convolutional neural network activations for one-dimensional time series is proposed and used. These analyses help further the understanding of the morphological differences between cosmic string cusp signals and blip glitches. Because of its classification speed in the order of magnitude of milliseconds, the deep-learning model is suitable for future use as part of a real-time detection pipeline. The deep-learning model is transverse and can therefore potentially be applied to other transient searches., Comment: 15 pages, 20 figures

We develop a robust and self-consistent framework to extract and classify gravitational wave candidates from noisy data, for the purpose of assisting in real-time multi-messenger follow-ups during LIGO-Virgo-KAGRA's fourth observing run~(O4). Our formalism implements several improvements to the low latency calculation of the probability of astrophysical origin~(\PASTRO{}), so as to correctly account for various factors such as the sensitivity change between observing runs, and the deviation of the recovered template waveform from the true gravitational wave signal that can strongly bias said calculation. We demonstrate the high accuracy with which our new formalism recovers and classifies gravitational wave triggers, by analyzing replay data from previous observing runs injected with simulated sources of different categories. We show that these improvements enable the correct identification of the majority of simulated sources, many of which would have otherwise been misclassified. We carry out the aforementioned analysis by implementing our formalism through the \GSTLAL{} search pipeline even though it can be used in conjunction with potentially any matched filtering pipeline. Armed with robust and self-consistent \PASTRO{} values, the \GSTLAL{} pipeline can be expected to provide accurate source classification information for assisting in multi-messenger follow-up observations to gravitational wave alerts sent out during O4., Comment: v2 upload was accidental. revert back to v1

Starting from May 2023, the LIGO Scientific, Virgo and KAGRA Collaboration is planning to conduct the fourth observing run with improved detector sensitivities and an expanded detector network including KAGRA. Accordingly, it is vital to optimize the detection algorithm of low-latency search pipelines, increasing their sensitivities to gravitational waves from compact binary coalescences. In this work, we discuss several new features developed for ranking statistics of GstLAL-based inspiral pipeline, which mainly consist of: the signal contamination removal, the bank-$\xi^2$ incorporation, the upgraded $\rho-\xi^2$ signal model and the integration of KAGRA. An injection study demonstrates that these new features improve the pipeline's sensitivity by approximately 15% to 20%, paving the way to further multi-messenger observations during the upcoming observing run., Comment: 13pages, 6figures

GstLAL is a stream-based matched-filtering search pipeline aiming at the prompt discovery of gravitational waves from compact binary coalescences such as the mergers of black holes and neutron stars. Over the past three observation runs by the LIGO, Virgo, and KAGRA (LVK) collaboration, the GstLAL search pipeline has participated in several tens of gravitational wave discoveries. The fourth observing run (O4) is set to begin in May 2023 and is expected to see the discovery of many new and interesting gravitational wave signals which will inform our understanding of astrophysics and cosmology. We describe the current configuration of the GstLAL low-latency search and show its readiness for the upcoming observation run by presenting its performance on a mock data challenge. The mock data challenge includes 40 days of LIGO Hanford, LIGO Livingston, and Virgo strain data along with an injection campaign in order to fully characterize the performance of the search. We find an improved performance in terms of detection rate and significance estimation as compared to that observed in the O3 online analysis. The improvements are attributed to several incremental advances in the likelihood ratio ranking statistic computation and the method of background estimation., Comment: 19 pages, 21 figures

We introduce a novel method to generate a bank of gravitational-waveform templates of binary black hole (BBH) mergers for matched-filter searches in LIGO, Virgo and Kagra data.We derive a novel expression for the metric approximation to the distance between templates, which is suitable for precessing BBHs and/or systems with higher-order modes (HM) imprints and we use it to meaningfully define a template probability density across the parameter space. We employ a masked autoregressive normalizing flow model which can be conveniently trained to quickly reproduce the target probability distribution and sample templates from it. Thanks to the normalizing flow, our code takes a few {\it hours} to produce random template banks with millions of templates, making it particularly suitable for high-dimensional spaces, such as those associated to precession, eccentricity and/or HM. After validating the performance of our method, we generate a bank for precessing black holes and a bank for aligned-spin binaries with HMs: with only 5% of the injections with fitting factor below the target of 0.97, we show that both banks cover satisfactorily the space. Our publicly released code mbank will enable searches of high-dimensional regions of BBH signal space, hitherto unfeasible due to the prohibitive cost of bank generation.

Matched-filtering gravitational wave search pipelines identify gravitational wave signals by computing correlations, i.e., signal-to-noise ratios, between gravitational wave detector data and gravitational wave template waveforms. Intrinsic parameters, the component masses and spins, of the gravitational wave waveforms are often stored in "template banks", and the construction of a densely populated template bank is essential for some gravitational wave search pipelines. This paper presents a template bank that is currently being used by the GstLAL-based compact binary search pipeline in the fourth observing run of the LIGO, Virgo, and KAGRA collaboration, and was generated with a new binary tree approach of placing templates, {\fontfamily{qcr}\selectfont manifold}. The template bank contains $1.8 \times 10^6$ sets of template parameters covering plausible neutron star and black hole systems up to a total mass of $400$ $M_\odot$ with component masses between $1$-$200$ $M_\odot$ and mass ratios between $1$ and $20$ under the assumption that each component object's angular momentum is aligned with the orbital angular momentum. We validate the template bank generated with our new method, {\fontfamily{qcr}\selectfont manifold}, by comparing it with a template bank generated with the previously used stochastic template placement method. We show that both template banks have similar effectualness. The {\fontfamily{qcr}\selectfont GstLAL} search pipeline performs singular value decomposition (SVD) on the template banks to reduce the number of filters used. We describe a new grouping of waveforms that improves the computational efficiency of SVD by nearly $5$ times as compared to previously reported SVD sorting schemes.

We demonstrate a new geometric method for fast template placement for searches for gravitational waves from the inspiral, merger and ringdown of compact binaries. The method is based on a binary tree decomposition of the template bank parameter space into non-overlapping hypercubes. We use a numerical approximation of the signal overlap metric at the center of each hypercube to estimate the number of templates required to cover the hypercube and determine whether to further split the hypercube. As long as the expected number of templates in a given cube is greater than a given threshold, we split the cube along its longest edge according to the metric. When the expected number of templates in a given hypercube drops below this threshold, the splitting stops and a template is placed at the center of the hypercube. Using this method, we generate aligned-spin template banks covering the mass range suitable for a search of Advanced LIGO data. The aligned-spin bank required ~24 CPU-hours and produced 2 million templates. In general, we find that other methods, namely stochastic placement, produces a more strictly bounded loss in match between waveforms, with the same minimal match between waveforms requiring about twice as many templates with our proposed algorithm. Though we note that the average match is higher, which would lead to a higher detection efficiency. Our primary motivation is not to strictly minimize the number of templates with this algorithm, but rather to produce a bank with useful geometric properties in the physical parameter space coordinates. Such properties are useful for population modeling and parameter estimation.

Current searches for gravitational waves (GWs) from black hole binaries using the LIGO and Virgo observatories are limited to analytical models for systems with black hole spins aligned (or anti-aligned) with the orbital angular momentum of the binary. Detecting black hole binaries with precessing spinsis crucial for gaining unique astrophysical insights into the formation of these sources. Therefore, it is essential to develop a search strategy capable of identifying compact binaries with precessing spins. Aligned-spin waveform models are inadequate for detecting compact binaries with high precessing spins. While several efforts have been made to construct template banks for detecting precessing binaries using matched filtering, this approach requires many templates to cover the entire search parameter space, significantly increasing the computational cost. This work explores the detection of GW signals from binary black holes(BBH) with both aligned and precessing spins using a convolutional neural network (CNN). We frame the detection of GW signals from aligned or precessing BBH systems as a hierarchical binary classification problem. The first CNN model classifies strain data as either pure noise or noisy signals (GWs from BBH). A second CNN model then classifies the detected noisy signal data as originating from either precessing or non-precessing (aligned/anti-aligned) systems. Using simulated data, the trained classifier distinguishes between noise and noisy GW signals with more than 99% accuracy. The second classifier further differentiates between aligned and highly precessing signals with around 95% accuracy. We extended our analysis to a multi-detector framework by performing a coincident test. Additionally, we tested the performance of our trained architecture on data from the first three observation runs of LIGO to identify detected BBH events as either aligned or precessing.

In the field of gravitational-wave (GW) interferometers, the most severe limitation to the detection of transient signals from astrophysical sources comes from transient noise artefacts, known as glitches, that happens at a rate around $1$ per minute. Because glitches reduce the amount of scientific data available, there is a need for better modelling and inclusion of glitches in large-scale studies, such as stress testing the search pipelines and increasing the confidence of detection. In this work, we employ a Generative Adversarial Network (GAN) to produce a particular class of glitches ({\it blip}) in the time domain. We share the trained network through a user-friendly open-source software package called \texttt{gengli} and provide practical examples of its usage., Comment: 6 pages, 4 figures

Our local motion with respect to the cosmic frame of rest is believed to be dominantly responsible for the observed dipole anisotropy in the Cosmic Microwave Background Radiation (CMBR). We study the effect of this motion on the sky distribution of gravitational wave (GW) sources. We determine the resulting dipole anisotropy in GW source number counts, mass weighted number counts, which we refer to as mass intensity, and mean mass per source. The mass M dependence of the number density n(M) distribution of BBH is taken directly from the data. We also test the anisotropy in the observable mean mass per source along the direction of the CMB dipole. The current data sample is relatively small and consistent with isotropy. The number of sources required for this test is likely to become available in the near future.

The noise of gravitational-wave (GW) interferometers limits their sensitivity and impacts the data quality, hindering the detection of GW signals from astrophysical sources. For transient searches, the most problematic are transient noise artifacts, known as glitches, that happen at a rate around $ 1 \text{ min}^{-1}$, and can mimic GW signals. Because of this, there is a need for better modeling and inclusion of glitches in large-scale studies, such as stress testing the pipelines. In this proof-of concept work we employ Generative Adversarial Networks (GAN), a state-of-the-art Deep Learning algorithm inspired by Game Theory, to learn the underlying distribution of blip glitches and to generate artificial populations. We reconstruct the glitch in the time-domain, providing a smooth input that the GAN can learn. With this methodology, we can create distributions of $\sim 10^{3}$ glitches from Hanford and Livingston detectors in less than one second. Furthermore, we employ several metrics to measure the performance of our methodology and the quality of its generations. This investigation will be extended in the future to different glitch classes with the final goal of creating an open-source interface for mock data generation., Comment: 17 pages, 21 figures to be published in Physical Review D

Fast, highly accurate, and reliable inference of the sky origin of gravitational waves would enable real-time multi-messenger astronomy. Current Bayesian inference methodologies, although highly accurate and reliable, are slow. Deep learning models have shown themselves to be accurate and extremely fast for inference tasks on gravitational waves, but their output is inherently questionable due to the blackbox nature of neural networks. In this work, we join Bayesian inference and deep learning by applying importance sampling on an approximate posterior generated by a multi-headed convolutional neural network. The neural network parametrizes Von Mises-Fisher and Gaussian distributions for the sky coordinates and two masses for given simulated gravitational wave injections in the LIGO and Virgo detectors. We generate skymaps for unseen gravitational-wave events that highly resemble predictions generated using Bayesian inference in a few minutes. Furthermore, we can detect poor predictions from the neural network, and quickly flag them., Comment: 12 pages, 9 figures, 1 table

We present a novel gravitational wave detection algorithm that conducts a matched filter search stochastically across the compact binary parameter space rather than relying on a fixed bank of template waveforms. This technique is competitive with standard template-bank-driven pipelines in both computational cost and sensitivity. However, the complexity of the analysis is simpler allowing for easy configuration and horizontal scaling across heterogeneous grids of computers. To demonstrate the method we analyze approximately one month of public LIGO data from July 27 00:00 2017 UTC - Aug 25 22:00 2017 UTC and recover eight known confident gravitational wave candidates. We also inject simulated binary black hole (BBH) signals to demonstrate the sensitivity., Comment: 12 pages, 6 figures

The matched filtering paradigm is the mainstay of gravitational wave (GW) searches from astrophysical coalescing compact binaries. The compact binary coalescence (CBC) search pipelines perform the matched filter between the GW detector's data and a large set of analytical waveforms. However, the computational cost of performing matched filter is very high as the required number of the analytical waveforms is also high. Recently, various deep learning-based methods have been deployed to identify a GW signal in the detector output as an alternative to computationally expensive matched filtering techniques. In past work, the researchers have considered the detection of GW signal mainly as a classification problem, in which they train the deep learning-based architecture by considering the noise and the GW signal as two different classes. However, in this work, for the first time, we have combined the Convolutional Neural Network (CNN) and matched filter methods to reduce the computational cost of the search by reducing the number of matched filtering operations. We have implemented the CNN based architecture not only for classification of the signal but also to identify the location of the signal in the intrinsic parameter space. Identifying the location in which the detected signal lies enables us to perform the matched filter operations between the data and the analytical waveforms generated for the smaller region of the parameter space only - thereby reducing the computational cost of the search. We demonstrate our method for two-dimensional parameter space for stellar to high mass binary black hole systems. In particular, we are able to classify between pure noise and noisy BBH signals with 99% accuracy. Further, the detected signals have been sub-classified into patches in mass components with an average accuracy > 97%

The field of gravitational-wave astronomy has been opened up by gravitational-wave observations made with interferometric detectors. This review surveys the current state-of-the-art in gravitational-wave detectors and data analysis methods currently used by the Laser Interferometer Gravitational-Wave Observatory in the United States and the Virgo Observatory in Italy. These analysis methods will also be used in the recently completed KAGRA Observatory in Japan. Data analysis algorithms are developed to target one of four classes of gravitational waves. Short duration, transient sources include compact binary coalescences, and burst sources originating from poorly modelled or unanticipated sources. Long duration sources include sources which emit continuous signals of consistent frequency, and many unresolved sources forming a stochastic background. A description of potential sources and the search for gravitational waves from each of these classes are detailed., Comment: 45 Pages

We introduce a novel methodology for the operation of an early %warning alert system for gravitational waves. It is based on short convolutional neural networks. We focus on compact binary coalescences, for light, intermediate and heavy binary-neutron-star systems. The signals are 1-dimensional time series $-$ the whitened time-strain $-$ injected in Gaussian noise built from the power-spectral density of the LIGO detectors at design sensitivity. We build short 1-dimensional convolutional neural networks to detect these types of events by training them on part of the early inspiral. We show that such networks are able to retrieve these signals from a small portion of the waveform., Comment: 6 pages, 7 figures

We perform a search for binary black hole mergers with one subsolar mass black hole and a primary component above $2 M_\odot$ in the second observing run of LIGO/Virgo. Our analysis therefore extends previous searches into a mass region motivated by the presence of a peak in any broad mass distribution of primordial black holes (PBHs) around $[2-3] M_\odot$ coming from the equation of state reduction at the QCD transition. Four candidate events are found passing a false alarm rate (FAR) threshold of 2 per year, although none are statistically significant enough for being clear detections. We first derive model independent limits on the PBH merging rates assuming a null result of the search. Then we confront them to two recent scenarios in which PBHs can constitute up to the totality of the Dark Matter, explain LIGO/Virgo mergers and the possible observation of a stochastic gravitational-wave background by NANOGrav. We find that these models still pass the rate limits and conclude that the analysis of the O3 and O4 observing runs will be decisive to test the hypothesis of a primordial origin of black hole mergers., Comment: 5 pages, 5 figures, 1 table. Appendix: 3 pages, 3 figures, 1 table. Comments welcome

GW170817 has led to the first example of multi-messenger astronomy with observations from gravitational wave interferometers and electromagnetic telescopes combined to characterise the source. However, detections of the early inspiral phase by the gravitational wave detectors would allow the observation of the earlier stages of the merger in the electromagnetic band, improving multi-messenger astronomy and giving access to new information. In this paper, we introduce a new machine-learning-based approach to produce early-warning alerts for an inspiraling binary neutron star system, based only on the early inspiral part of the signal. We give a proof of concept to show the possibility to use a combination of small convolutional neural networks trained on the whitened detector strain in the time domain to detect and classify early inspirals. Each of those is targeting a specific range of chirp masses dividing the binary neutron star category into three sub-classes: light, intermediate and heavy. In this work, we focus on one LIGO detector at design sensitivity and generate noise from the design power spectral density. We show that within this setup it is possible to produce an early alert up to 100 seconds before the merger for the best-case scenario. We also present some future upgrades that will enhance the detection capabilities of our convolutional neural networks. Finally, we also show that the current number of detections for a realistic binary neutron star population is comparable to that of matched filtering and that there is a high probability to detect GW170817- and GW190425-like events at design sensitivity., Comment: 12 pages, 14 figures

The accurate localization of gravitational-wave (GW) events in low-latency is a crucial element in the search for further multimessenger signals from these cataclysmic events. The localization of these events in low-latency uses signal-to-noise ratio (SNR) time-series from matched-filtered searches which identify candidate events. Here we report on an improvement to the GstLAL-based inspiral pipeline, the low-latency pipeline that identified GW170817 and GW190425, which automates the use of SNRs from all detectors in the network in rapid localization of GW events. This improvement was incorporated into the detection pipeline prior to the recent third observing run of the Advanced LIGO and Advanced Virgo detector network. Previously for this pipeline, manual intervention was required to use SNRs from all detectors if a candidate GW event was below an SNR threshold for any detector in the network. The use of SNRs from subthreshold events can meaningfully decrease the area of the 90% confidence region estimated by rapid localization. To demonstrate this, we present a study of the simulated detections of $\mathcal{O}(2\times10^4)$ binary neutron stars using a network mirroring the second observational run of the Advanced LIGO and Virgo detectors. When incorporating subthreshold SNRs in rapid localization, we find that the fraction of events that can be localized down to $100~\mathrm{deg}^2$ or smaller increases by a factor 1.18.

We present updates to GstLAL, a matched filter gravitational-wave search pipeline, in Advanced LIGO and Virgo's third observing run. We discuss the incorporation of statistical data quality information into GstLAL's multi-dimensional likelihood ratio ranking statistic and additional improvements to search for gravitational wave candidates found in only one detector. Statistical data quality information is provided by iDQ, a data quality pipeline that infers the presence of short-duration transient noise in gravitational-wave data using the interferometer's auxiliary state, which has operated in near real-time since before LIGO's first observing run in 2015. We look at the performance and impact on noise rejection by the inclusion of iDQ information in GstLAL's ranking statistic, and discuss GstLAL results in the GWTC-2 catalog, focusing on two case studies; GW190424A, a single-detector gravitational-wave event found by GstLAL and a period of time in Livingston impacted by a thunderstorm., Comment: 8 pages, 5 figures

The GstLAL library, derived from Gstreamer and the LIGO Algorithm Library, supports a stream-based approach to gravitational-wave data processing. Although GstLAL was primarily designed to search for gravitational-wave signatures of merging black holes and neutron stars, it has also contributed to other gravitational-wave searches, data calibration, and detector-characterization efforts. GstLAL has played an integral role in all of the LIGO-Virgo collaboration detections, and its low-latency configuration has enabled rapid electromagnetic follow-up for dozens of compact binary candidates.

Background estimation is important for determining the statistical significance of a gravitational-wave event. Currently, the background model is constructed numerically from the strain data using estimation techniques that insulate the strain data from any potential signals. However, as the observation of gravitational-wave signals become frequent, the effectiveness of such insulation will decrease. Contamination occurs when signals leak into the background model. In this work, we demonstrate an improved background estimation technique for the searches of gravitational waves (GWs) from binary neutron star coalescences by time-reversing the modeled GW waveforms. We found that the new method can robustly avoid signal contamination at a signal rate of about one per 20 seconds and retain a clean background model in the presence of signals.

Binary neutron stars (BNSs) will spend $\simeq 10$ -- 15 minutes in the band of Advanced LIGO and Virgo detectors at design sensitivity. Matched-filtering of gravitational-wave (GW) data could in principle accumulate enough signal-to-noise ratio (SNR) to identify a forthcoming event tens of seconds before the companions collide and merge. Here we report on the design and testing of an early warning gravitational-wave detection pipeline. Early warning alerts can be produced for sources that are at low enough redshift so that a large enough SNR accumulates $\sim 10 - 60\,\rm s$ before merger. We find that about 7% (respectively, 49%) of the total detectable BNS mergers will be detected $60\, \rm s$ ($10\, \rm s$) before the merger. About 2% of the total detectable BNS mergers will be detected before merger and localized to within $100\, \rm \text{deg}^2$ (90% credible interval). Coordinated observing by several wide-field telescopes could capture the event seconds before or after the merger. LIGO-Virgo detectors at design sensitivity could facilitate observing at least one event at the onset of merger., Comment: small update in numbers caused by using a more updated local BNS rate estimate

The recently published GWTC-1 - a journal article summarizing the search for gravitational waves (GWs) from coalescing compact binaries in data produced by the LIGO-Virgo network of ground-based detectors during their first and second observing runs - quoted estimates for the rates of binary neutron star, neutron star black hole binary, and binary black hole mergers, as well as assigned probabilities of astrophysical origin for various significant and marginal GW candidate events. In this paper, we delineate the formalism used to compute these rates and probabilities, which assumes that triggers above a low ranking statistic threshold, whether of terrestrial or astrophysical origin, occur as independent Poisson processes. In particular, we include an arbitrary number of astrophysical categories by redistributing, via mass-based template weighting, the foreground probabilities of candidate events, across source classes. We evaluate this formalism on synthetic GW data, and demonstrate that this method works well for the kind of GW signals observed during the first and second observing runs., Comment: 19 pages, 5 figures

We present a search for gravitational waves from double neutron star binaries inspirals in Advanced LIGO's first observing run. The search considers a narrow range of binary chirp masses motivated by the population of known double neutron star binaries in the nearby universe. This search differs from previously published results by providing the most sensitive published survey of neutron stars in Advanced LIGO's first observing run within this narrow mass range and including times when only one of the two LIGO detectors was in operation in the analysis. The search was sensitive to binary neutron star inspirals to an average distance of ~85 Mpc over 93.2 days. We do not identify any unambiguous gravitational wave signals in our sample of 103 sub-threshold candidates with false-alarm-rates of less than one per day. However, given the expected binary neutron star merger rate of R = 100 - 4000 Gpc^(-3) yr^(-1), we expect O(1) gravitational wave events within our candidate list. This suggests the possibility that one or more of these candidates is in fact a binary neutron star merger. Although the contamination fraction in our candidate list is ~99%, it might be possible to correlate these events with other messengers to identify a potential multi-messenger signal. We provide an online candidate list with the times and sky locations for all events in order to enable multi-messenger searches.

After their successful first observing run (September 12, 2015 - January 12, 2016), the Advanced LIGO detectors were upgraded to increase their sensitivity for the second observing run (November 30, 2016 - August 26, 2017). The Advanced Virgo detector joined the second observing run on August 1, 2017. We discuss the updates that happened during this period in the GstLAL-based inspiral pipeline, which is used to detect gravitational waves from the coalescence of compact binaries both in low latency and an offline configuration. These updates include deployment of a zero-latency whitening filter to reduce the over-all latency of the pipeline by up to 32 seconds, incorporation of the Virgo data stream in the analysis, introduction of a single-detector search to analyze data from the periods when only one of the detectors is running, addition of new parameters to the likelihood ratio ranking statistic, increase in the parameter space of the search, and introduction of a template mass-dependent glitch-excision thresholding method., Comment: 12 pages, 7 figures, to be submitted to Phys. Rev. D, comments welcome

Gravitational waves searches for compact binary mergers with LIGO and Virgo are presently a two stage process. First, a gravitational wave signal is identified. Then, an exhaustive search over possible signal parameters is performed. It is critical that the identification stage is efficient in order to maximize the number of gravitational wave sources that are identified. Initial identification of gravitational wave signals with LIGO and Virgo happens in real-time which requires that less than one second of computational time must be used for each one second of gravitational wave data collected. In contrast, subsequent parameter estimation may require hundreds of hours of computational time to analyze the same one second of gravitational wave data. The real-time identification requirement necessitates efficient and often approximate methods for signal analysis. We describe one piece of real-time gravitational-wave identification: an efficient method for ascertaining a signal's consistency between multiple gravitational wave detectors suitable for real-time gravitational wave searches for compact binary mergers. This technique was used in analyses of Advanced LIGO's second observing run and Advanced Virgo's first observing run.

We describe the methods used to construct the aligned-spin template bank of gravitational waveforms used by the GstLAL-based inspiral pipeline to analyze data from the second observing run of Advanced LIGO and the first observing run of advanced Virgo. The bank expands upon the parameter space covered during the first observing run, including coverage for merging compact binary systems with total mass between 2 $\mathrm{M}_{\odot}$ and 400 $\mathrm{M}_{\odot}$ and mass ratios between 1 and 97.988. Thus the systems targeted include merging neutron star-neutron star systems, neutron star-black hole binaries, and black hole-black hole binaries expanding into the intermediate-mass range. Component masses less than 2 $\mathrm{M}_{\odot}$ have allowed (anti-)aligned spins between $\pm0.05$ while component masses greater than 2 $\mathrm{M}_{\odot}$ have allowed (anti-)aligned between $\pm0.999$. The bank placement technique combines a stochastic method with a new grid-bank method to better isolate noisy templates, resulting in a total of 677,000 templates., Comment: 9 pages, 13 figures

We describe a stream-based analysis pipeline to detect gravitational waves from the merger of binary neutron stars, binary black holes, and neutron-star-black-hole binaries within ~ 1 minute of the arrival of the merger signal at Earth. Such low-latency detection is crucial for the prompt response by electromagnetic facilities in order to observe any fading electromagnetic counterparts that might be produced by mergers involving at least one neutron star. Even for systems expected not to produce counterparts, low-latency analysis of the data is useful for deciding when not to point telescopes, and as feedback to observatory operations. Analysts using this pipeline were the first to identify GW151226, the second gravitational-wave event ever detected. The pipeline also operates in an offline mode, in which it incorporates more refined information about data quality and employs acausal methods that are inapplicable to the online mode. The pipeline's offline mode was used in the detection of the first two gravitational-wave events, GW150914 and GW151226, as well as the identification of a third candidate, LVT151012.

One century after its formulation, Einstein's general relativity has made remarkable predictions and turned out to be compatible with all experimental tests. Most of these tests probe the theory in the weak-field regime, and there are theoretical and experimental reasons to believe that general relativity should be modified when gravitational fields are strong and spacetime curvature is large. The best astrophysical laboratories to probe strong-field gravity are black holes and neutron stars, whether isolated or in binary systems. We review the motivations to consider extensions of general relativity. We present a (necessarily incomplete) catalog of modified theories of gravity for which strong-field predictions have been computed and contrasted to Einstein's theory, and we summarize our current understanding of the structure and dynamics of compact objects in these theories. We discuss current bounds on modified gravity from binary pulsar and cosmological observations, and we highlight the potential of future gravitational wave measurements to inform us on the behavior of gravity in the strong-field regime., Comment: 188 pages, 46 figures, 6 tables, 903 references. Matches version published in Classical and Quantum Gravity. Supplementary data files available at http://www.phy.olemiss.edu/~berti/research/ and http://centra.tecnico.ulisboa.pt/network/grit/files/

Searches for gravitational waves produced by coalescing black hole binaries with total masses $\gtrsim25\,$M$_\odot$ use matched filtering with templates of short duration. Non-Gaussian noise bursts in gravitational wave detector data can mimic short signals and limit the sensitivity of these searches. Previous searches have relied on empirically designed statistics incorporating signal-to-noise ratio and signal-based vetoes to separate gravitational wave candidates from noise candidates. We report on sensitivity improvements achieved using a multivariate candidate ranking statistic derived from a supervised machine learning algorithm. We apply the random forest of bagged decision trees technique to two separate searches in the high mass $\left( \gtrsim25\,\mathrm{M}_\odot \right)$ parameter space. For a search which is sensitive to gravitational waves from the inspiral, merger, and ringdown (IMR) of binary black holes with total mass between $25\,$M$_\odot$ and $100\,$M$_\odot$, we find sensitive volume improvements as high as $70_{\pm 13}-109_{\pm 11}$\% when compared to the previously used ranking statistic. For a ringdown-only search which is sensitive to gravitational waves from the resultant perturbed intermediate mass black hole with mass roughly between $10\,$M$_\odot$ and $600\,$M$_\odot$, we find sensitive volume improvements as high as $61_{\pm 4}-241_{\pm 12}$\% when compared to the previously used ranking statistic. We also report how sensitivity improvements can differ depending on mass regime, mass ratio, and available data quality information. Finally, we describe the techniques used to tune and train the random forest classifier that can be generalized to its use in other searches for gravitational waves.