Compact Binary Coalescences Research Articles

Rapid likelihood free inference of compact binary coalescences using accelerated hardware

Abstract We report a gravitational-wave parameter estimation algorithm, AMPLFI, based on likelihood-free inference using normalizing flows. The focus of AMPLFI is to perform real-time parameter estimation for candidates detected by machine-learning based compact binary coalescence search, Aframe. We present details of our algorithm and optimizations done related to data-loading and pre-processing on accelerated hardware. We train our model using binary black-hole (BBH) simulations on real LIGO-Virgo detector noise. Our model has ∼6 million trainable parameters with training times ≦ 24 hours. Based on online deployment on a mock data stream of LIGO-Virgo data, Aframe+AMPLFI is able to pick up BBH candidates and infer parameters for real-time alerts from data acquisition with a net latency of ∼6 seconds.

Quasinormal modes of slowly-spinning horizonless compact objects

One of the main predictions of general relativity is the existence of black holes featuring a horizon beyond which nothing can escape. Gravitational waves from the remnants of compact binary coalescences have the potential to probe new physics close to the black hole horizons. This prospect is of particular interest given several quantum-gravity models that predict the presence of horizonless and singularity-free compact objects. The membrane paradigm is a generic framework that allows one to parametrize the interior of compact objects in terms of the properties of a fictitious fluid located at the object’s radius. It has been used to derive the quasinormal mode spectrum of static horizonless compact objects. Extending the membrane paradigm to rotating objects is crucial to constrain the properties of the spinning merger remnants. In this work, we extend the membrane paradigm to linear order in spin and use it to analyze the relationships between the quasinormal modes, the object’s reflectivity, and the membrane parameters. We find a breaking of isospectrality between axial and polar modes when the object is partially reflecting or the compactness differs from the black hole case. We also find that in reflective ultracompact objects some of the modes tend toward instability as the spin increases. Finally, we show that the spin enhances the deviations from the black-hole quasinormal mode spectrum as the compactness decreases. This implies that spinning horizonless compact objects may be more easily differentiated than nonspinning ones in the prompt ringdown. Published by the American Physical Society 2024

Mitigating the Binary Viewing Angle Bias for Standard Sirens

The inconsistency between experiments in the measurements of the local Universe expansion rate, the Hubble constant, suggests unknown systematics in the existing experiments or new physics. Gravitational-wave standard sirens, a method to independently provide direct measurements of the Hubble constant, have the potential to address this tension. Before that, it is critical to ensure there are no substantial systematics in the standard siren method. A significant systematic has been identified when the viewing angle of the gravitational-wave sources, the compact binary coalescences, was inferred inaccurately from electromagnetic observations of the sources. Such a systematic has led to a more than 10% discrepancy in the standard siren Hubble constant measurements with the observations of binary neutron star merger, GW170817. In this Letter, we develop a new formalism to infer and mitigate this systematic. We demonstrate that the systematic uncertainty of the Hubble constant measurements can be reduced to a level smaller than their statistical uncertainty with 5, 10, and 20 binary neutron star merger observations. We show that our formalism successfully reduces the systematics even if the shape of the biased viewing angle distribution does not follow precisely the model we choose. Our formalism ensures unbiased standard siren Hubble constant measurements when the binary viewing angles are inferred from electromagnetic observations.

Extension of the characterization of non-Gaussianity in gravitational wave detectors with a statistical hypothesis test

Abstract In gravitational wave (GW) astronomy, non-Gaussian noise, such as scattered light noise disturbs stable interferometer operation, limiting the interferometer’s sensitivity, and reducing the reliability of the analyses. In scattered light noise, the non-Gaussian noise dominates the sensitivity in a low frequency range of less than a few hundred Hz, which is sensitive to GWs from compact binary coalescence. This non-Gaussian noise prevents reliable parameter estimation, since several analysis methods are optimized only for Gaussian noise. Therefore, identifying data contaminated by non-Gaussian noise is important. In this work, we extended the conventional method to evaluate non-Gaussian noise, the Rayleigh statistic, by using a statistical hypothesis test to determine a threshold for non-Gaussian noise. First, we estimated the distribution of the Rayleigh statistic against Gaussian noise, called the background distribution, and validated that our extension serves as the hypothetical test. The threshold on the Rayleigh statistic is estimated at 0.73 and 1.28 when the significance level is 0.05, and the sample size is 39. Moreover, we investigated the detection efficiency by assuming two non-Gaussian noise models. For example, for the model with strong scattered light noise, the true positive rate (TPR) was always above 0.7 when the significance level was 0.05. For the demonstration, we applied our extension with the estimated thresholds to the real data. We confirmed our extension provides the binarized outputs of the statistical tests and contributes to finding the non-Gaussian noise in the real data. The results showed that our extension can contribute to an initial detection of non-Gaussian noise and lead to further investigation of the origin of the non-Gaussian noise.

Identifying noise transients in gravitational-wave data arising from nonlinear couplings

Abstract Noise in various interferometer systems can sometimes couple non-linearly to create excess noise in the gravitational wave (GW) strain data. Third-order statistics, such as bicoherence and biphase, can identify these couplings and help discriminate those occurrences from astrophysical GW signals. However, the conventional analysis can yield large bicoherence values even when no phase-coupling is present, thereby, resulting in false identifications. Introducing artificial phase randomization in computing the bicoherence reduces such occurrences with negligible impact on its effectiveness for detecting true phase-coupled disturbances. We demonstrate this property with simulated disturbances – focusing only on short-duration ones (lasting up to a few seconds) in this work. Statistical hypothesis testing is used for distinguishing phase-coupled disturbances from non-phase coupled ones when employing the phase-randomized bicoherence. We also obtain an expression for the bicoherence value that minimizes the sum of the probabilities of false positives and false negatives. This can be chosen as a threshold for shortlisting bicoherence triggers for further scrutiny for the presence of non-linear coupling. Finally, the utility of the phase-randomized bicoherence analysis in GW time-series data is demonstrated for the following three scenarios: (1) Finding third-order statistical similarities within categories of noise transients, such as blips and koi fish. If these non-Gaussian noise transients, or glitches, have a common source, their bicoherence maps can have similarities arising from common bifrequencies related to that source. (2) Differentiating linear or non-linear phase-coupled glitches from compact binary coalescence signals through their bicoherence maps. This is explained with a simulated signal. (3) Identifying repeated bifrequencies in the second and third observation runs (i.e., O2 and O3) of LIGO and Virgo.

Optimized search for a binary black hole merger population in LIGO-Virgo O3 data

Maximizing the number of detections in matched filter searches for compact binary coalescence (CBC) gravitational wave signals requires a model of the source population distribution. In previous searches using the framework, sensitivity to the population of binary black hole (BBH) mergers was improved by restricting the range of filter template mass ratios and use of a simple one-dimensional population model. However, this approach does not make use of our full knowledge of the population and cannot be extended to a full parameter space search. Here, we introduce a new ranking method, based on kernel density estimation with adaptive bandwidth, to accurately model the probability distributions of binary source parameters over a template bank, both for signals and for noise events. We demonstrate this ranking method by conducting a search over LIGO-Virgo O3 data for BBHs with unrestricted mass ratio, using a signal model derived from previous significant detected events. We achieve over 10% increase in sensitive volume for a simple power-law simulated signal population, compared to the previous BBH search. Correspondingly, with the new ranking, eight additional candidate events above an inverse false alarm rate threshold 0.5 yr are identified. Published by the American Physical Society 2024

GW230529_181500: a potential primordial binary black hole merger in the mass gap

During the fourth observing run of the LIGO-Virgo-KAGRA detector network, the LIGO Livingston observatory detected a coalescing compact binary, GW230529_181500, with component masses of 2.5–4.5 M ⊙ and 1.2–2.0 M ⊙ at the 90% credible level. The gravitational-wave data alone is insufficient to determine whether the components are neutron stars or black holes. In this paper, we propose that GW230529_181500 originated from the merger of two primordial black holes (PBHs). We estimate a merger rate of 5.0+47.0 -4.9 Gpc-3 yr-1 for compact binary coalescences with properties similar to GW230529_181500. Assuming the source is a PBH-PBH merger, GW230529_181500-like events lead to approximately 1.7+36.2 -1.5 × 10-3 of the dark matter in the form of PBHs. The required abundance of PBHs to explain this event is consistent with existing upper limits derived from microlensing, cosmic microwave background observations and the null detection of gravitational-wave background by LIGO-Virgo-KAGRA.

Observation of Gravitational Waves from the Coalescence of a 2.5–4.5 M ⊙ Compact Object and a Neutron Star

We report the observation of a coalescing compact binary with component masses 2.5–4.5 M ⊙ and 1.2–2.0 M ⊙ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO–Virgo–KAGRA detector network on 2023 May 29 by the LIGO Livingston observatory. The primary component of the source has a mass less than 5 M ⊙ at 99% credibility. We cannot definitively determine from gravitational-wave data alone whether either component of the source is a neutron star or a black hole. However, given existing estimates of the maximum neutron star mass, we find the most probable interpretation of the source to be the coalescence of a neutron star with a black hole that has a mass between the most massive neutron stars and the least massive black holes observed in the Galaxy. We provisionally estimate a merger rate density of 55−47+127Gpc−3yr−1 for compact binary coalescences with properties similar to the source of GW230529_181500; assuming that the source is a neutron star–black hole merger, GW230529_181500-like sources may make up the majority of neutron star–black hole coalescences. The discovery of this system implies an increase in the expected rate of neutron star–black hole mergers with electromagnetic counterparts and provides further evidence for compact objects existing within the purported lower mass gap.

Analysis of the subsolar-mass black hole candidate SSM200308 from the second part of the third observing run of Advanced LIGO-Virgo

We present a follow-up study of a subsolar black hole candidate identified in the second part of the third observing run of the LIGO-Virgo-KAGRA collaboration. The candidate was identified by the GstLAL search pipeline in the Hanford and Livingston LIGO detectors with a network signal-to-noise ratio of 8.90 and a false-alarm-rate of 1 per 5 years. It is the most significant of the three candidates found below the O3b subsolar mass false-alarm rate threshold of 2 per year, but still not significant enough above the background to claim a clear gravitational wave origin. A Bayesian parameter estimation of this candidate, denoted SSM200308, reveals that if the signal originates from a compact binary coalescence, the component masses are m1=0.62−0.20+0.46M⊙ and m2=0.27−0.10+0.12M⊙ (90% credible intervals) with at least one component being firmly subsolar, below the minimum mass of a neutron star. This discards the hypothesis that the signal comes from a standard binary neutron star. The signal coherence test between the two LIGO detectors is consistent with, but does not necessarily imply, a compact object coalescence origin.

Constraining Possible γ-Ray Burst Emission from GW230529 Using Swift-BAT and Fermi-GBM

GW230529 is the first compact binary coalescence detected by the LIGO–Virgo–KAGRA collaboration with at least one component mass confidently in the lower mass gap, corresponding to the range 3–5 M ⊙. If interpreted as a neutron star–black hole merger, this event has the most symmetric mass ratio detected so far and therefore has a relatively high probability of producing electromagnetic (EM) emission. However, no EM counterpart has been reported. At the merger time t 0, Swift-BAT and Fermi-GBM together covered 100% of the sky. Performing a targeted search in a time window [t 0 − 20 s, t 0 + 20 s], we report no detection by the Swift-BAT and Fermi-GBM instruments. Combining the position-dependent γ-ray flux upper limits and the gravitational-wave posterior distribution of luminosity distance, sky localization, and inclination angle of the binary, we derive constraints on the characteristic luminosity and structure of the jet possibly launched during the merger. Assuming a top-hat jet structure, we exclude at 90% credibility the presence of a jet that has at the same time an on-axis isotropic luminosity ≳1048 erg s−1 in the bolometric band 1 keV–10 MeV and a jet opening angle ≳15°. Similar constraints are derived by testing other assumptions about the jet structure profile. Excluding GRB 170817A, the luminosity upper limits derived here are below the luminosity of any GRB observed so far.

Calibrating gravitational-wave search algorithms with conformal prediction

In astronomy, we frequently face the decision problem: does this data contain a signal? Typically, a statistical approach is used, which requires a threshold. The choice of threshold presents a common challenge in settings where signals and noise must be delineated, but their distributions overlap. Gravitational-wave astronomy, which has gone from the first discovery to catalogs of hundreds of events in less than a decade, presents a fascinating case study. For signals from colliding compact objects, the field has evolved from a frequentist to a Bayesian methodology. However, the issue of choosing a threshold and validating noise contamination in a catalog persists. Confusion and debate often arise due to the misapplication of statistical concepts, the complicated nature of the detection statistics, and the inclusion of astrophysical background models. We introduce conformal prediction (CP), a framework developed in machine learning to provide distribution-free uncertainty quantification to point predictors. We show that CP can be viewed as an extension of the traditional statistical frameworks whereby thresholds are calibrated such that the uncertainty intervals are statistically rigorous and the error rate can be validated. Moreover, we discuss how CP offers a framework to optimally build a metapipeline combining the outputs from multiple independent searches. We introduce CP with a toy cosmic-ray detector, which captures the salient features of most astrophysical search problems and allows us to demonstrate the features of CP in a simple context. We then apply the approach to a recent gravitational-wave mock data challenge using multiple search algorithms for compact binary coalescence signals in interferometric gravitational-wave data. Finally, we conclude with a discussion on the future potential of the method for gravitational-wave astronomy. Published by the American Physical Society 2024

Searches for compact binary coalescence events using neural networks in LIGO/Virgo third observation period

We present the results on the search for the coalescence of compact binary mergers using convolutional neural networks (CNNs) and the LIGO/Virgo data for the O3 observation period. Two-dimensional images in time and frequency are used as input. The analysis is performed in three separate mass regions covering the range for the masses in the binary system from 0.2 M ⊙ to 100 M ⊙ , excluding very asymmetric mass configurations. We explore neural networks trained with input information from pairs of interferometers or all three interferometers together, concluding that the use of the maximum information available leads to an improved performance. A scan over the O3 data set, using the CNNs, is performed with different false rate thresholds for claiming detection of at most one event per year or at most one event per week. The latter would correspond to a loose online selection still leading to affordable false alarm rates. The efficiency of the neutral networks to detect the O3 catalog events is discussed. In the case of a false rate threshold of at most one event per week, the scan leads to the detection of about 50% of the O3 catalog events. Once the search is limited to the catalog events within the mass range used for neural networks training, the detection efficiency increases up to 70 % . Further improvement in search efficiency, using the same type of algorithms, will require the implementation of new criteria to suppress the remaining major background sources.

Forecast cosmological constraints from the number counts of Gravitational Waves events

We present a forecast for the upcoming Einstein Telescope (ET) interferometer with two new methods to infer cosmological parameters. We consider the emission of Gravitational Waves (GWs) from compact binary coalescences, whose electromagnetic counterpart is missing, namely Dark Sirens events. Most of the methods used to infer cosmological information from GW observations rely on the availability of a redshift measurement, usually obtained with the help of external data, such as galaxy catalogues used to identify the most likely galaxy to host the emission of the observed GWs. Instead, our approach is based only on the GW survey itself and exploits the information on the distance of the GW rather than on its redshift. Since a large dataset spanning the whole distance interval is expected to fully represent the distribution, we applied our methods to the expected ET's far-reaching measuring capabilities. We simulate a dataset of observations with ET using the package darksirens, assuming an underlying ΛCDM cosmology, and including the possibility to choose between three possible Star Formation Rate density (SFR) models, also accounting for possible population III stars (PopIII). We test two independent statistical methods: one based on a likelihood approach on the theoretical expectation of observed events, and another applying the cut-and-count method, a simpler method to compare the observed number of events with the predicted counts. Both methods are consistent in their final results, and also show the potential to distinguish an incorrect SFR model from the data, but not the presence of a possible PopIII. Concerning the cosmological parameters, we find instead that ET observations by themselves would suffer from strong degeneracies, but have the potential to significantly contribute to parameter estimation if used in synergy with other surveys.

GWAK: gravitational-wave anomalous knowledge with recurrent autoencoders

Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such techniques have been traditionally used in searches for compact binary coalescences (CBCs), and have been employed in all known GW detections so far. However, interesting science cases aside from compact mergers do not yet have accurate enough modeling to make matched filtering possible, including core-collapse supernovae and sources where stochasticity may be involved. Therefore the development of techniques to identify sources of these types is of significant interest. In this paper, we present a method of anomaly detection based on deep recurrent autoencoders to enhance the search region to unmodeled transients. We use a semi-supervised strategy that we name ‘Gravitational Wave Anomalous Knowledge’ (GWAK). While the semi-supervised approach to this problem entails a potential reduction in accuracy compared to fully supervised methods, it offers a generalizability advantage by enhancing the reach of experimental sensitivity beyond the constraints of pre-defined signal templates. We construct a low-dimensional embedded space using the GWAK method, capturing the physical signatures of distinct signals on each axis of the space. By introducing signal priors that capture some of the salient features of GW signals, we allow for the recovery of sensitivity even when an unmodeled anomaly is encountered. We show that regions of the GWAK space can identify CBCs, detector glitches and also a variety of unmodeled astrophysical sources.

Bayesian real-time classification of multi-messenger electromagnetic and gravitational-wave observations

Because of the electromagnetic (EM) radiation produced during the merger, compact binary coalescences with neutron stars may result in multi-messenger observations. In order to follow up on the gravitational-wave (GW) signal with EM telescopes, it is critical to promptly identify the properties of these sources. This identification must rely on the properties of the progenitor source, such as the component masses and spins, as determined by low-latency detection pipelines in real time. The output of these pipelines, however, might be biased, which could decrease the accuracy of parameter recovery. Machine learning algorithms are used to correct this bias. In this work, we revisit this problem and discuss two new implementations of supervised machine learning algorithms, K-nearest neighbors and random forest, which are able to predict the presence of a neutron star and post-merger matter remnant in low-latency compact binary coalescence searches across different search pipelines and data sets. Additionally, we present a novel approach for calculating the Bayesian probabilities for these two metrics. Instead of metric scores derived from binary machine learning classifiers, our scheme is designed to provide the astronomy community well-defined probabilities. This would deliver a more direct and easily interpretable product to assist EM telescopes in deciding whether to follow up on GW events in real time.

GSpyNetTree: a signal-vs-glitch classifier for gravitational-wave event candidates

Despite achieving sensitivities capable of detecting the extremely small amplitude of gravitational waves (GWs), LIGO and Virgo detector data contain frequent bursts of non-Gaussian transient noise, commonly known as ‘glitches’. Glitches come in various time-frequency morphologies, and they are particularly challenging when they mimic the form of real GWs. Given the higher expected event rate in the next observing run (O4), LIGO-Virgo GW event candidate validation will require increased levels of automation. Gravity Spy, a machine learning tool that successfully classified common types of LIGO and Virgo glitches in previous observing runs, has the potential to be restructured as a compact binary coalescence (CBC) signal-vs-glitch classifier to accurately distinguish between glitches and GW signals. A CBC signal-vs-glitch classifier used for automation must be robust and compatible with a broad array of background noise, new sources of glitches, and the likely occurrence of overlapping glitches and GWs. We present GSpyNetTree, the Gravity Spy Convolutional Neural Network Decision Tree: a multi-CNN classifier using CNNs in a decision tree sorted via total GW candidate mass tested under these realistic O4-era scenarios.

WaveFormer: transformer-based denoising method for gravitational-wave data

With the advent of gravitational-wave astronomy and the discovery of more compact binary coalescences, data quality improvement techniques are desired to handle the complex and overwhelming noise in gravitational wave (GW) observational data. Though recent machine learning-based studies have shown promising results for data denoising, they are unable to precisely recover both the GW signal amplitude and phase. To address such an issue, we develop a deep neural network centered workflow, WaveFormer, for significant noise suppression and signal recovery on observational data from the Laser Interferometer Gravitational-Wave Observatory (LIGO). The WaveFormer has a science-driven architecture design with hierarchical feature extraction across a broad frequency spectrum. As a result, the overall noise and glitch are decreased by more than one order of magnitude and the signal recovery error is roughly 1% and 7% for the phase and amplitude, respectively. Moreover, on 75 reported binary black hole events of LIGO we obtain a significant improvement of inverse false alarm rate. Our work highlights the potential of large neural networks in GW data analysis and, while primarily demonstrated on LIGO data, its adaptable design indicates promise for broader application within the International Gravitational-Wave Observatories Network in future observational runs.

Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences

Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences

GWTC-2.1: Deep extended catalog of compact binary coalescences observed by LIGO and Virgo during the first half of the third observing run

GWTC-2.1: Deep extended catalog of compact binary coalescences observed by LIGO and Virgo during the first half of the third observing run

Rapid localization and inference on compact binary coalescences with the Advanced LIGO-Virgo-KAGRA gravitational-wave detector network

Rapid localization and inference on compact binary coalescences with the Advanced LIGO-Virgo-KAGRA gravitational-wave detector network